marsabit county - inequalities

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Exploring Kenya’s Inequality

A PUBLICATION OF KNBS AND SID

© 2013 Kenya National Bureau of Statistics (KNBS) and Society for International Development (SID)

ISBN – 978 - 9966 - 029 - 18 - 8

With funding from DANIDA through Drivers of Accountability Programme

The publication, however, remains the sole responsibility of the Kenya National Bureau of Statistics (KNBS) and the Society for International Development (SID).

Written by: Eston Ngugi

Data and tables generation: Samuel Kipruto

Paul Samoei

Maps generation: George Matheka Kamula

Technical Input and Editing: Katindi Sivi-Njonjo

Jason Lakin

Copy Editing: Ali Nadim Zaidi

Leonard Wanyama

Design, Print and Publishing: Ascent Limited

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form, or by any means electronic, mechanical, photocopying, recording or otherwise, without the prior express and written permission of the publishers. Any part of this publication may be freely reviewed or quoted provided the source is duly acknowledged. It may not be sold or used for commercial purposes or for profit.

Kenya National Bureau of Statistics

P.O. Box 30266-00100 Nairobi, Kenya

Email: [email protected] Website: www.knbs.or.ke

Society for International Development – East Africa

P.O. Box 2404-00100 Nairobi, Kenya

Email: [email protected] | Website: www.sidint.net

Published by

iii

Pulling Apart or Pooling Together?

Table of contents Table of contents iii

Foreword iv

Acknowledgements v

Striking features on inter-county inequalities in Kenya vi

List of Figures viii

List Annex Tables ix

Abbreviations xi

Introduction 2

Marsabit County 9

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ForewordKenya, like all African countries, focused on poverty alleviation at independence, perhaps due to the level of

vulnerability of its populations but also as a result of the ‘trickle down’ economic discourses of the time, which

assumed that poverty rather than distribution mattered – in other words, that it was only necessary to concentrate

on economic growth because, as the country grew richer, this wealth would trickle down to benefit the poorest

sections of society. Inequality therefore had a very low profile in political, policy and scholarly discourses. In

recent years though, social dimensions such as levels of access to education, clean water and sanitation are

important in assessing people’s quality of life. Being deprived of these essential services deepens poverty and

reduces people’s well-being. Stark differences in accessing these essential services among different groups

make it difficult to reduce poverty even when economies are growing. According to the Economist (June 1, 2013),

a 1% increase in incomes in the most unequal countries produces a mere 0.6 percent reduction in poverty. In the

most equal countries, the same 1% growth yields a 4.3% reduction in poverty. Poverty and inequality are thus part

of the same problem, and there is a strong case to be made for both economic growth and redistributive policies.

From this perspective, Kenya’s quest in vision 2030 to grow by 10% per annum must also ensure that inequality

is reduced along the way and all people benefit equitably from development initiatives and resources allocated.

Since 2004, the Society for International Development (SID) and Kenya National Bureau of Statistics (KNBS) have

collaborated to spearhead inequality research in Kenya. Through their initial publications such as ‘Pulling Apart:

Facts and Figures on Inequality in Kenya,’ which sought to present simple facts about various manifestations

of inequality in Kenya, the understanding of Kenyans of the subject was deepened and a national debate on

the dynamics, causes and possible responses started. The report ‘Geographic Dimensions of Well-Being in

Kenya: Who and Where are the Poor?’ elevated the poverty and inequality discourse further while the publication

‘Readings on Inequality in Kenya: Sectoral Dynamics and Perspectives’ presented the causality, dynamics and

other technical aspects of inequality.

KNBS and SID in this publication go further to present monetary measures of inequality such as expenditure

patterns of groups and non-money metric measures of inequality in important livelihood parameters like

employment, education, energy, housing, water and sanitation to show the levels of vulnerability and patterns of

unequal access to essential social services at the national, county, constituency and ward levels.

We envisage that this work will be particularly helpful to county leaders who are tasked with the responsibility

of ensuring equitable social and economic development while addressing the needs of marginalized groups

and regions. We also hope that it will help in informing public engagement with the devolution process and

be instrumental in formulating strategies and actions to overcome exclusion of groups or individuals from the

benefits of growth and development in Kenya.

It is therefore our great pleasure to present ‘Exploring Kenya’s inequality: Pulling apart or pooling together?’

Ali Hersi Society for International Development (SID) Regional Director

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AcknowledgementsKenya National Bureau of Statistics (KNBS) and Society for International Development (SID) are grateful

to all the individuals directly involved in the publication of ‘Exploring Kenya’s Inequality: Pulling Apart or

Pulling Together?’ books. Special mention goes to Zachary Mwangi (KNBS, Ag. Director General) and

Ali Hersi (SID, Regional Director) for their institutional leadership; Katindi Sivi-Njonjo (SID, Progrmme

Director) and Paul Samoei (KNBS) for the effective management of the project; Eston Ngugi; Tabitha

Wambui Mwangi; Joshua Musyimi; Samuel Kipruto; George Kamula; Jason Lakin; Ali Zaidi; Leonard

Wanyama; and Irene Omari for the different roles played in the completion of these publications.

KNBS and SID would like to thank Bernadette Wanjala (KIPPRA), Mwende Mwendwa (KIPPRA), Raphael

Munavu (CRA), Moses Sichei (CRA), Calvin Muga (TISA), Chrispine Oduor (IEA), John T. Mukui, Awuor

Ponge (IPAR, Kenya), Othieno Nyanjom, Mary Muyonga (SID), Prof. John Oucho (AMADPOC), Ms. Ada

Mwangola (Vision 2030 Secretariat), Kilian Nyambu (NCIC), Charles Warria (DAP), Wanjiru Gikonyo

(TISA) and Martin Napisa (NTA), for attending the peer review meetings held on 3rd October 2012 and

Thursday, 28th Feb 2013 and for making invaluable comments that went into the initial production and

the finalisation of the books. Special mention goes to Arthur Muliro, Wambui Gathathi, Con Omore,

Andiwo Obondoh, Peter Gunja, Calleb Okoyo, Dennis Mutabazi, Leah Thuku, Jackson Kitololo, Yvonne

Omwodo and Maureen Bwisa for their institutional support and administrative assistance throughout the

project. The support of DANIDA through the Drivers of Accountability Project in Kenya is also gratefully

acknowledged.

Stefano PratoManaging Director,SID

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Striking Features on Intra-County Inequality in Kenya Inequalities within counties in all the variables are extreme. In many cases, Kenyans living within a

single county have completely different lifestyles and access to services.

Income/expenditure inequalities1. The five counties with the worst income inequality (measured as a ratio of the top to the bottom

decile) are in Coast. The ratio of expenditure by the wealthiest to the poorest is 20 to one and above

in Lamu, Tana River, Kwale, and Kilifi. This means that those in the top decile have 20 times as much

expenditure as those in the bottom decile. This is compared to an average for the whole country of

nine to one.

2. Another way to look at income inequality is to compare the mean expenditure per adult across

wards within a county. In 44 of the 47 counties, the mean expenditure in the poorest wards is less

than 40 percent the mean expenditure in the wealthiest wards within the county. In both Kilifi and

Kwale, the mean expenditure in the poorest wards (Garashi and Ndavaya, respectively) is less than

13 percent of expenditure in the wealthiest ward in the county.

3. Of the five poorest counties in terms of mean expenditure, four are in the North (Mandera, Wajir,

Turkana and Marsabit) and the last is in Coast (Tana River). However, of the five most unequal

counties, only one (Marsabit County) is in the North (looking at ratio of mean expenditure in richest

to poorest ward). The other four most unequal counties by this measure are: Kilifi, Kwale, Kajiado

and Kitui.

4. If we look at Gini coefficients for the whole county, the most unequal counties are also in Coast:

Tana River (.631), Kwale (.604), and Kilifi (.570).

5. The most equal counties by income measure (ratio of top decile to bottom) are: Narok, West Pokot,

Bomet, Nandi and Nairobi. Using the ratio of average income in top to bottom ward, the five most

equal counties are: Kirinyaga, Samburu, Siaya, Nyandarua, Narok.

Access to Education6. Major urban areas in Kenya have high education levels but very large disparities. Mombasa, Nairobi

and Kisumu all have gaps between highest and lowest wards of nearly 50 percentage points in

share of residents with secondary school education or higher levels.

7. In the 5 most rural counties (Baringo, Siaya, Pokot, Narok and Tharaka Nithi), education levels

are lower but the gap, while still large, is somewhat lower than that espoused in urban areas. On

average, the gap in these 5 counties between wards with highest share of residents with secondary

school or higher and those with the lowest share is about 26 percentage points.

8. The most extreme difference in secondary school education and above is in Kajiado County where

the top ward (Ongata Rongai) has nearly 59 percent of the population with secondary education

plus, while the bottom ward (Mosiro) has only 2 percent.

9. One way to think about inequality in education is to compare the number of people with no education

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to those with some education. A more unequal county is one that has large numbers of both. Isiolo

is the most unequal county in Kenya by this measure, with 51 percent of the population having

no education, and 49 percent with some. This is followed by West Pokot at 55 percent with no

education and 45 percent with some, and Tana River at 56 percent with no education and 44 with

some.

Access to Improved Sanitation10. Kajiado County has the highest gap between wards with access to improved sanitation. The best

performing ward (Ongata Rongai) has 89 percent of residents with access to improved sanitation

while the worst performing ward (Mosiro) has 2 percent of residents with access to improved

sanitation, a gap of nearly 87 percentage points.

11. There are 9 counties where the gap in access to improved sanitation between the best and worst

performing wards is over 80 percentage points. These are Baringo, Garissa, Kajiado, Kericho, Kilifi,

Machakos, Marsabit, Nyandarua and West Pokot.

Access to Improved Sources of Water 12. In all of the 47 counties, the highest gap in access to improved water sources between the county

with the best access to improved water sources and the least is over 45 percentage points. The

most severe gaps are in Mandera, Garissa, Marsabit, (over 99 percentage points), Kilifi (over 98

percentage points) and Wajir (over 97 percentage points).

Access to Improved Sources of Lighting13. The gaps within counties in access to electricity for lighting are also enormous. In most counties

(29 out of 47), the gap between the ward with the most access to electricity and the least access

is more than 40 percentage points. The most severe disparities between wards are in Mombasa

(95 percentage point gap between highest and lowest ward), Garissa (92 percentage points), and

Nakuru (89 percentage points).

Access to Improved Housing14. The highest extreme in this variable is found in Baringo County where all residents in Silale ward live

in grass huts while no one in Ravine ward in the same county lives in grass huts.

Overall ranking of the variables15. Overall, the counties with the most income inequalities as measured by the gini coefficient are Tana

River, Kwale, Kilifi, Lamu, Migori and Busia. However, the counties that are consistently mentioned

among the most deprived hence have the lowest access to essential services compared to others

across the following nine variables i.e. poverty, mean household expenditure, education, work for

pay, water, sanitation, cooking fuel, access to electricity and improved housing are Mandera (8

variables), Wajir (8 variables), Turkana (7 variables) and Marsabit (7 variables).

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Abbreviations

AMADPOC African Migration and Development Policy Centre

CRA Commission on Revenue Allocation

DANIDA Danish International Development Agency

DAP Drivers of Accountability Programme

EAs Enumeration Areas

HDI Human Development Index

IBP International Budget Partnership

IEA Institute of Economic Affairs

IPAR Institute of Policy Analysis and Research

KIHBS Kenya Intergraded Household Budget Survey

KIPPRA Kenya Institute for Public Policy Research and Analysis

KNBS Kenya National Bureau of Statistics

LPG Liquefied Petroleum Gas

NCIC National Cohesion and Integration Commission

NTA National Taxpayers Association

PCA Principal Component Analysis

SAEs Small Area Estimation

SID Society for International Development

TISA The Institute for Social Accountability

VIP latrine Ventilated-Improved Pit latrine

VOCs Volatile Organic Carbons

WDR World Development Report

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IntroductionBackgroundFor more than half a century many people in the development sector in Kenya have worked at alleviating

extreme poverty so that the poorest people can access basic goods and services for survival like food,

safe drinking water, sanitation, shelter and education. However when the current national averages are

disaggregated there are individuals and groups that still lag too behind. As a result, the gap between

the rich and the poor, urban and rural areas, among ethnic groups or between genders reveal huge

disparities between those who are well endowed and those who are deprived.

According to the world inequality statistics, Kenya was ranked 103 out of 169 countries making it the

66th most unequal country in the world. Kenya’s Inequality is rooted in its history, politics, economics

and social organization and manifests itself in the lack of access to services, resources, power, voice

and agency. Inequality continues to be driven by various factors such as: social norms, behaviours and

practices that fuel discrimination and obstruct access at the local level and/ or at the larger societal

level; the fact that services are not reaching those who are most in need of them due to intentional or

unintentional barriers; the governance, accountability, policy or legislative issues that do not favor equal

opportunities for the disadvantaged; and economic forces i.e. the unequal control of productive assets

by the different socio-economic groups.

According to the 2005 report on the World Social Situation, sustained poverty reduction cannot be

achieved unless equality of opportunity and access to basic services is ensured. Reducing inequality

must therefore be explicitly incorporated in policies and programmes aimed at poverty reduction. In

addition, specific interventions may be required, such as: affirmative action; targeted public investments

in underserved areas and sectors; access to resources that are not conditional; and a conscious effort

to ensure that policies and programmes implemented have to provide equitable opportunities for all.

This chapter presents the basic concepts on inequality and poverty, methods used for analysis,

justification and choice of variables on inequality. The analysis is based on the 2009 Kenya housing

and population census while the 2006 Kenya integrated household budget survey is combined with

census to estimate poverty and inequality measures from the national to the ward level. Tabulation of

both money metric measures of inequality such as mean expenditure and non-money metric measures

of inequality in important livelihood parameters like, employment, education, energy, housing, water

and sanitation are presented. These variables were selected from the census data and analyzed in

detail and form the core of the inequality reports. Other variables such as migration or health indicators

like mortality, fertility etc. are analyzed and presented in several monographs by Kenya National Bureau

of Statistics and were therefore left out of this report.

MethodologyGini-coefficient of inequalityThis is the most commonly used measure of inequality. The coefficient varies between ‘0’, which reflects

complete equality and ‘1’ which indicates complete inequality. Graphically, the Gini coefficient can be

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easily represented by the area between the Lorenz curve and the line of equality. On the figure below,

the Lorenz curve maps the cumulative income share on the vertical axis against the distribution of the

population on the horizontal axis. The Gini coefficient is calculated as the area (A) divided by the sum

of areas (A and B) i.e. A/(A+B). If A=0 the Gini coefficient becomes 0 which means perfect equality,

whereas if B=0 the Gini coefficient becomes 1 which means complete inequality. Let xi be a point on

the X-axis, and yi a point on the Y-axis, the Gini coefficient formula is:

�=

�� +��=N

iiiii yyxxGini

111 ))((1 .

An Illustration of the Lorenz Curve

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

LORENZ CURVE

Cum

ulat

ive

% o

f Exp

endi

ture

Cumulative % of Population

A

B

Small Area Estimation (SAE)The small area problem essentially concerns obtaining reliable estimates of quantities of interest —

totals or means of study variables, for example — for geographical regions, when the regional sample

sizes are small in the survey data set. In the context of small area estimation, an area or domain

becomes small when its sample size is too small for direct estimation of adequate precision. If the

regional estimates are to be obtained by the traditional direct survey estimators, based only on the

sample data from the area of interest itself, small sample sizes lead to undesirably large standard errors

for them. For instance, due to their low precision the estimates might not satisfy the generally accepted

publishing criteria in official statistics. It may even happen that there are no sample members at all from

some areas, making the direct estimation impossible. All this gives rise to the need of special small area

estimation methodology.

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Most of KNBS surveys were designed to provide statistically reliable, design-based estimates only at

the national, provincial and district levels such as the Kenya Intergraded Household Budget Survey

of 2005/06 (KIHBS). The sheer practical difficulties and cost of implementing and conducting sample

surveys that would provide reliable estimates at levels finer than the district were generally prohibitive,

both in terms of the increased sample size required and in terms of the added burden on providers of

survey data (respondents). However through SAE and using the census and other survey datasets,

accurate small area poverty estimates for 2009 for all the counties are obtainable.

The sample in the 2005/06 KIHBS, which was a representative subset of the population, collected

detailed information regarding consumption expenditures. The survey gives poverty estimate of urban

and rural poverty at the national level, the provincial level and, albeit with less precision, at the district

level. However, the sample sizes of such household surveys preclude estimation of meaningful poverty

measures for smaller areas such as divisions, locations or wards. Data collected through censuses

are sufficiently large to provide representative measurements below the district level such as divisions,

locations and sub-locations. However, this data does not contain the detailed information on consumption

expenditures required to estimate poverty indicators. In small area estimation methodology, the first step

of the analysis involves exploring the relationship between a set of characteristics of households and

the welfare level of the same households, which has detailed information about household expenditure

and consumption. A regression equation is then estimated to explain daily per capita consumption

and expenditure of a household using a number of socio-economic variables such as household size,

education levels, housing characteristics and access to basic services.

While the census does not contain household expenditure data, it does contain these socio-economic

variables. Therefore, it will be possible to statistically impute household expenditures for the census

households by applying the socio-economic variables from the census data on the estimated

relationship based on the survey data. This will give estimates of the welfare level of all households

in the census, which in turn allows for estimation of the proportion of households that are poor and

other poverty measures for relatively small geographic areas. To determine how many people are

poor in each area, the study would then utilize the 2005/06 monetary poverty lines for rural and urban

households respectively. In terms of actual process, the following steps were undertaken:

Cluster Matching: Matching of the KIHBS clusters, which were created using the 1999 Population and

Housing Census Enumeration Areas (EA) to 2009 Population and Housing Census EAs. The purpose

was to trace the KIBHS 2005/06 clusters to the 2009 Enumeration Areas.

Zero Stage: The first step of the analysis involved finding out comparable variables from the survey

(Kenya Integrated Household Budget 2005/06) and the census (Kenya 2009 Population and Housing

Census). This required the use of the survey and census questionnaires as well as their manuals.

First Stage (Consumption Model): This stage involved the use of regression analysis to explore the

relationship between an agreed set of characteristics in the household and the consumption levels of

the same households from the survey data. The regression equation was then used to estimate and

explain daily per capita consumption and expenditure of households using socio-economic variables

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such as household size, education levels, housing characteristics and access to basic services, and

other auxiliary variables. While the census did not contain household expenditure data, it did contain

these socio-economic variables.

Second Stage (Simulation): Analysis at this stage involved statistical imputation of household

expenditures for the census households, by applying the socio-economic variables from the census

data on the estimated relationship based on the survey data.

Identification of poor households Principal Component Analysis (PCA)In order to attain the objective of the poverty targeting in this study, the household needed to be

established. There are three principal indicators of welfare; household income; household consumption

expenditures; and household wealth. Household income is the theoretical indicator of choice of welfare/

economic status. However, it is extremely difficult to measure accurately due to the fact that many

people do not remember all the sources of their income or better still would not want to divulge this

information. Measuring consumption expenditures has many drawbacks such as the fact that household

consumption expenditures typically are obtained from recall method usually for a period of not more

than four weeks. In all cases a well planned and large scale survey is needed, which is time consuming

and costly to collect. The estimation of wealth is a difficult concept due to both the quantitative as well

as the qualitative aspects of it. It can also be difficult to compute especially when wealth is looked at as

both tangible and intangible.

Given that the three main indicators of welfare cannot be determined in a shorter time, an alternative

method that is quick is needed. The alternative approach then in measuring welfare is generally through

the asset index. In measuring the asset index, multivariate statistical procedures such the factor analysis,

discriminate analysis, cluster analysis or the principal component analysis methods are used. Principal

components analysis transforms the original set of variables into a smaller set of linear combinations

that account for most of the variance in the original set. The purpose of PCA is to determine factors (i.e.,

principal components) in order to explain as much of the total variation in the data as possible.

In this project the principal component analysis was utilized in order to generate the asset (wealth)

index for each household in the study area. The PCA can be used as an exploratory tool to investigate

patterns in the data; in identify natural groupings of the population for further analysis and; to reduce

several dimensionalities in the number of known dimensions. In generating this index information from

the datasets such as the tenure status of main dwelling units; roof, wall, and floor materials of main

dwelling; main source of water; means of human waste disposal; cooking and lighting fuels; household

items such radio TV, fridge etc was required. The recent available dataset that contains this information

for the project area is the Kenya Population and Housing Census 2009.

There are four main approaches to handling multivariate data for the construction of the asset index

in surveys and censuses. The first three may be regarded as exploratory techniques leading to index

construction. These are graphical procedures and summary measures. The two popular multivariate

procedures - cluster analysis and principal component analysis (PCA) - are two of the key procedures

that have a useful preliminary role to play in index construction and lastly regression modeling approach.

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In the recent past there has been an increasing routine application of PCA to asset data in creating

welfare indices (Gwatkin et al. 2000, Filmer and Pritchett 2001 and McKenzie 2003).

Concepts and definitionsInequalityInequality is characterized by the existence of unequal opportunities or life chances and unequal

conditions such as incomes, goods and services. Inequality, usually structured and recurrent, results

into an unfair or unjust gap between individuals, groups or households relative to others within a

population. There are several methods of measuring inequality. In this study, we consider among

other methods, the Gini-coefficient, the difference in expenditure shares and access to important basic

services.

Equality and EquityAlthough the two terms are sometimes used interchangeably, they are different concepts. Equality

requires all to have same/ equal resources, while equity requires all to have the same opportunity to

access same resources, survive, develop, and reach their full potential, without discrimination, bias, or

favoritism. Equity also accepts differences that are earned fairly.

PovertyThe poverty line is a threshold below which people are deemed poor. Statistics summarizing the bottom

of the consumption distribution (i.e. those that fall below the poverty line) are therefore provided. In

2005/06, the poverty line was estimated at Ksh1,562 and Ksh2,913 per adult equivalent1 per month

for rural and urban households respectively. Nationally, 45.2 percent of the population lives below the

poverty line (2009 estimates) down from 46 percent in 2005/06.

Spatial DimensionsThe reason poverty can be considered a spatial issue is two-fold. People of a similar socio-economic

background tend to live in the same areas because the amount of money a person makes usually, but

not always, influences their decision as to where to purchase or rent a home. At the same time, the area

in which a person is born or lives can determine the level of access to opportunities like education and

employment because income and education can influence settlement patterns and also be influenced

by settlement patterns. They can therefore be considered causes and effects of spatial inequality and

poverty.

EmploymentAccess to jobs is essential for overcoming inequality and reducing poverty. People who cannot access

productive work are unable to generate an income sufficient to cover their basic needs and those of

their families, or to accumulate savings to protect their households from the vicissitudes of the economy.

1This is basically the idea that every person needs different levels of consumption because of their age, gender, height, weight, etc. and therefore we take this into account to create an adult equivalent based on the average needs of the different populations

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The unemployed are therefore among the most vulnerable in society and are prone to poverty. Levels

and patterns of employment and wages are also significant in determining degrees of poverty and

inequality. Macroeconomic policy needs to emphasize the need for increasing regular good quality

‘work for pay’ that is covered by basic labour protection. The population and housing census 2009

included questions on labour and employment for the population aged 15-64.

The census, not being a labour survey, only had few categories of occupation which included work

for pay, family business, family agricultural holdings, intern/volunteer, retired/home maker, full time

student, incapacitated and no work. The tabulation was nested with education- for none, primary and

secondary level.

EducationEducation is typically seen as a means of improving people’s welfare. Studies indicate that inequality

declines as the average level of educational attainment increases, with secondary education producing

the greatest payoff, especially for women (Cornia and Court, 2001). There is considerable evidence

that even in settings where people are deprived of other essential services like sanitation or clean

water, children of educated mothers have much better prospects of survival than do the children of

uneducated mothers. Education is therefore typically viewed as a powerful factor in leveling the field of

opportunity as it provides individuals with the capacity to obtain a higher income and standard of living.

By learning to read and write and acquiring technical or professional skills, people increase their chances

of obtaining decent, better-paying jobs. Education however can also represent a medium through

which the worst forms of social stratification and segmentation are created. Inequalities in quality and

access to education often translate into differentials in employment, occupation, income, residence and

social class. These disparities are prevalent and tend to be determined by socio-economic and family

background. Because such disparities are typically transmitted from generation to generation, access

to educational and employment opportunities are to a certain degree inherited, with segments of the

population systematically suffering exclusion. The importance of equal access to a well-functioning

education system, particularly in relation to reducing inequalities, cannot be overemphasized.

WaterAccording to UNICEF (2008), over 1.1 billion people lack access to an improved water source and over

three million people, mostly children, die annually from water-related diseases. Water quality refers

to the basic and physical characteristics of water that determines its suitability for life or for human

uses. The quality of water has tremendous effects on human health both in the short term and in the

long term. As indicated in this report, slightly over half of Kenya’s population has access to improved

sources of water.

SanitationSanitation refers to the principles and practices relating to the collection, removal or disposal of human

excreta, household waste, water and refuse as they impact upon people and the environment. Decent

sanitation includes appropriate hygiene awareness and behavior as well as acceptable, affordable and

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sustainable sanitation services which is crucial for the health and wellbeing of people. Lack of access

to safe human waste disposal facilities leads to higher costs to the community through pollution of

rivers, ground water and higher incidence of air and water borne diseases. Other costs include reduced

incomes as a result of disease and lower educational outcomes.

Nationally, 61 percent of the population has access to improved methods of waste disposal. A sizeable

population i.e. 39 percent of the population is disadvantaged. Investments made in the provision of

safe water supplies need to be commensurate with investments in safe waste disposal and hygiene

promotion to have significant impact.

Housing Conditions (Roof, Wall and Floor)Housing conditions are an indicator of the degree to which people live in humane conditions. Materials

used in the construction of the floor, roof and wall materials of a dwelling unit are also indicative of the

extent to which they protect occupants from the elements and other environmental hazards. Housing

conditions have implications for provision of other services such as connections to water supply,

electricity, and waste disposal. They also determine the safety, health and well being of the occupants.

Low provision of these essential services leads to higher incidence of diseases, fewer opportunities

for business services and lack of a conducive environment for learning. It is important to note that

availability of materials, costs, weather and cultural conditions have a major influence on the type of

materials used.

Energy fuel for cooking and lightingLack of access to clean sources of energy is a major impediment to development through health related

complications such as increased respiratory infections and air pollution. The type of cooking fuel or

lighting fuel used by households is related to the socio-economic status of households. High level

energy sources are cleaner but cost more and are used by households with higher levels of income

compared with primitive sources of fuel like firewood which are mainly used by households with a lower

socio-economic profile. Globally about 2.5 billion people rely on biomass such as fuel-wood, charcoal,

agricultural waste and animal dung to meet their energy needs for cooking.

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Marsabit County

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Marsabit CountyFigure 25.1: Marsabit Population Pyramid

PopulationMarsabit County has a child rich population, where 0-14 year olds constitute 47% of the total population. This is due to high fertility rates among women as shown by the highest percentage of household size of 4-6 members at 41%.

Employment The 2009 population and housing census covered in brief the labour status as tabulated below. The main variable of interest for inequality discussed in the text is work for pay by level of education. The other variables, notably family business, family agricultural holdings, intern/volunteer, retired/homemaker, fulltime student, incapacitated and no work are tabulated and presented in the annex table 25.3 up to ward level.

Table 25: Overall Employment by Education Levels in Marsabit County

Education LevelWork for pay

Family Business

Family Agricul-tural Holding

Intern/ Volunteer

Retired/ Home-maker

Fulltime Student Incapacitated No work

Number of Individuals

Total 9.9 10.6 48.2 1.0 12.7 8.4 0.6 8.6 141,236

None 6.0 11.1 58.5 0.9 14.6 0.4 0.7 7.8 100,796

Primary 12.5 10.5 27.1 1.1 10.3 27.7 0.4 10.5 25,272

Secondary+ 31.5 7.6 14.5 1.8 3.7 29.4 0.3 11.3 15,168

In Marsabit County 6% of the residents with no formal education are working for pay, 13% of those with a primary education and 32% of those with a secondary level of education or above. Work for pay is highest in Nairobi at 49% and this is 17 percentage points above the level in Marsabit for those with a secondary level of education or above.

15 10 520

11


Gini Coefficient In this report, the Gini index measures the extent to which the distribution of consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of ‘0’ represents perfect equality, while an index of ‘1’ implies perfect inequality. Marsabit County’s Gini index is 0.365 compared with Turkana County, which has the least inequality nationally (0.283).

Figure 25.2: Marsabit County-Gini Coefficient by Ward

12



EducationFigure 25.3: Marsabit County-Percentage of Population by Education Attainment by Ward

TURBI

MAIKONA

DUKANA

OBBU

NORTH HORR

URAN

ILLERET

LAISAMIS

KARGI/SOUTH HORR

GOLBO

LOIYANGALANI

KORR/NGURUNIT

LOGOLOGO

KARARE

BUTIYE

SAGANTE/JALDESA

MARSABIT CENTRAL

³

Location of MarsabitCounty in Kenya

Percentage of Population by Education Attainment - Ward Level - Marsabit County

Legend

NonePrimary

County Boundary

Secondary and aboveWater Bodies

0 60 12030 Kilometers

Only 6% of Marsabit County residents have a secondary level of education or above. Saku constituency has the highest share of residents with a secondary level of education or above at 12%. This is four times North Horr constituency, which has the lowest share of residents with a secondary level of education or above. Saku constit-uency is 6 percentage points above the county average. Marsabit Central ward has the highest share of residents with a secondary level of education or above at 18%. This is 17 percentage points above Illeret ward, which has the lowest share of residents with a secondary level of education or above. Marsabit Central ward is 12 percent-age points above the county average.

A total of 26% of Marsabit County residents have a primary level of education only. Saku constituency has the highest share of residents with a primary level of education only at 41%. This is almost three times above North Horr constit-uency, which has the lowest share of residents with a primary level of education only. Saku constituency is 15 per-centage points above the county average. Marsabit Central ward has the highest share of residents with a primary level of education only at 47%. This is 12 percentage points above Illeret ward, which has the lowest share of residents with a primary level of education only. Marsabit Central ward is 21 percentage points above the county average.

A total of 68% of Marsabit County residents have no formal education. North Horr constituency has the highest share of residents with no formal education at 84%. This is almost two times Saku constituency, which has the lowest share of residents with no formal education. North Horr constituency is 16 percentage points above the county average. Illeret ward has the highest percentage of residents with no formal education at 95%. This is al-most three times Marsabit Central ward, which has the lowest percentage of residents with no formal education. Illeret ward is 27 percentage points above the county average.

13


EnergyCooking Fuel

Figure 25.4: Percentage Distribution of Households by Source of Cooking Fuel in Marsabit County

Less than 1% of residents in Marsabit County use Liquefied Petroleum Gas (LPG), and 1% use paraffin. 92% use firewood and 6% use charcoal. Firewood is the most common cooking fuel by gender; 91% in male headed households and 93% in female headed households.

North Horr constituency has the highest level of firewood use in Marsabit County at 98%. This is 15 percentage points above Saku constituency, which has the smallest share at 83%. North Horr constituency is 6 percentage points above the county average. Dukana ward has the highest level of firewood use in Marsabit County at 100%. This is four times Moyale Township ward, which has the lowest share at 25%. This is 8 percentage points above the county average.

Moyale constituency has the highest level of charcoal use in Marsabit County at 11%. This is 10 percentage points above North Horr constituency, which has the lowest share at 1%. Moyale constituency is about 5 percentage points above the county average. Moyale Township ward has the highest level of charcoal use in Marsabit County at 64%. This is 64 percentage points more than Dukana ward, which has the lowest share. Moyale Township ward is 58 percentage points above the county average.

Saku constituency has the highest level of use of paraffin in Marsabit County at 5%. This is 5 percentage points above Laisamis constituency which has the lowest share of paraffin use. Saku constituency is 4 percentage points higher than the county average. Marsabit Central ward has the highest level of use of paraffin in Marsabit County at 9%. This is 9% points above Loiyangalani that has no level of paraffin use. Marsabit Central Ward is 8% above the county average.

Lighting

Figure 25.5: Percentage Distribution of Households by Source of Lighting Fuel in Marsabit County

14



Only 8% of residents in Marsabit County use electricity as their main source of lighting. A further 23% use lanterns, and 12% use tin lamps. 47% use fuel wood. Electricity use is slightly common in male headed households at 8% as compared with female headed households at 7%.

Saku constituency has the highest level of electricity use at 19%.This is 19 percentage points above Laisamis and North Horr constituencies, which have the lowest level of electricity use. Saku constituency is 11 percentage points above the county average. Moyale Township ward has the highest level of electricity use at 60%. This is 60% points above Turbi, Logologo and Laisamis ward that have no levels of electricity use. Moyale Township ward is 52 percentage points above the county average.

HousingFlooring

In Marsabit County, 17% of residents have homes with cement floors, while 82% have earth floors. Less than 1% has wood or tile floors. Moyale Constituency with the highest share of cement floors at 32%.That is 16 times North Horr constituency has the lowest share of cement floors. Moyale constituency is 15 percentage points above the county average. Moyale Township ward has the highest share of cement floors at 85%.That is 84 percentage points above Dukana ward, which has the lowest share of cement floors. Moyale Township ward is 68 percentage points above the county average.

Figure 25.6: Percentage Distribution of Households by Floor Material in Marsabit County

Roofing

15


Figure 25.7: Percentage Distribution of Households by Roof Material in Marsabit County

In Marsabit County, less than 1% of residents have homes with concrete roofs, while 28% have corrugated iron roofs. Grass and makuti roofs constitute 35% of homes, and 1% has mud/dung roofs.

Saku constituency has the highest share of corrugated iron sheet roofs at 62%.That is almost nine times North Horr constituency, which has the lowest share of corrugated iron sheet roofs. Saku constituency is 34 percentage points above the county average. Two wards, Marsabit Central and Moyale Township, have the highest share of corrugated iron sheet roofs at 91% each. That is almost 23 times Korr/Ngurunit ward, which has the lowest share of corrugated iron sheet roofs. Marsabit Central and Moyale Township ward is 63 percentage points above the county average.

Moyale constituency has the highest share of grass/makuti roofs at 49%.That is three times North Horr constitu-ency, which has the lowest share of grass/makuti roofs. Moyale constituency is 14 percentage points above the county average. Golbo ward, which has the highest share of grass/makuti roofs at 81%. This is 80 percentage points above Moyale Township ward that the lowest share. Golbo ward is 46 percentage points above the county average.

Walls

Figure 25.8: Percentage Distribution of Households by Wall Material in Marsabit County

16



In Marsabit County, 7% of homes have either brick or stone walls. 42% of homes have mud/wood or mud/cement walls. 8% have wood walls. 1% has corrugated iron walls. 11% has grass/thatched walls. 32% have tin or other walls.

Moyale constituency, which has the highest share of brick/stone walls at 11%. That is almost six times North Horr constituency, which has the lowest share of brick/stone walls. Moyale constituency is 4 percentage points above the county average. Moyale Township ward has the highest share of brick/stone walls at 49%.That is 49 percentage points above Obbu ward, which has the lowest share of brick/stone walls. Moyale Township ward is 42 percentage points above the county average.

Saku constituency has the highest share of mud with wood/cement walls at 83%.That is almost eight times North Horr constituency, which has the lowest share of mud with wood/cement. Saku constituency is 41 percentage points above the county average. Two wards, Sololo and Karare, have the highest share of mud with wood/cement walls 90% each. That is almost 23 times Laisamis ward, which has the lowest share of mud with wood/cement walls. Sololo and Karare ward is 48 percentage points above the county average.

WaterImproved sources of water comprise protected spring, protected well, borehole, piped into dwelling, piped and rain water collection while unimproved sources include pond, dam, lake, stream/river, unprotected spring, unpro-tected well, jabia, water vendor and others.

In Marsabit County, 38% of residents use improved sources of water, with the rest relying on unimproved sources. Use of improved sources is higher in female headed households at 41% compared with male headed households at 36%.

Moyale constituency has the highest share of residents using improved sources of water at 45%. That is almost twice North Horr constituency, which has the lowest share using improved sources of water. Moyale constituency is 7 percentage points above the county average. Logologo ward has the highest share of residents using im-proved sources of water at 100%. That is 99 percentage points above Dukana ward, which has the lowest share using improved sources of water. Logologo ward is 62 percentage points above the county average.

17


Figure 25.9: Marsabit County-Percentage of Households with Improved and Unimproved Sources of Water by Ward

TURBI

MAIKONA

DUKANA

OBBU

NORTH HORR

URAN

ILLERET

LAISAMIS

KARGI/SOUTH HORR

GOLBO

LOIYANGALANI

KORR/NGURUNIT

LOGOLOGO

KARARE

BUTIYE

SAGANTE/JALDESAMARSABIT CENTRAL

SOLOLO

³

Percentage of Households with Improved and UnimprovedSource of Water - Ward Level - Marsabit County



Legend

Unimproved Source of WaterImproved Source of waterWater Bodies

County Boundary

SanitationA total of 27% of residents in Marsabit County use improved sanitation, while the rest use unimproved sanitation. There is no significant gender differential in use of improved sanitation, at 26% in male headed households and 29% in female headed households.

Saku constituency has the highest share of residents using improved sanitation at 48%.That is five times Laisamis constituency, which has the lowest share using improved sanitation. Saku constituency is 21 percentage points above the county average. Moyale Township ward has the highest share of residents using improved sanitation at 88%.That is almost 44 times Illeret ward, which has the lowest share using improved sanitation. Moyale Township is 61 percentage points above the county average.

18



Figure 25.10: Marsabit County –Percentage of Households with Improved and Unimproved Sanita-tion by Ward

TURBI

MAIKONA

DUKANA

OBBU

NORTH HORR

URAN

ILLERET

LAISAMIS

KARGI/SOUTH HORR

GOLBO

LOIYANGALANI

KORR/NGURUNIT

LOGOLOGO

KARARE

BUTIYE

SAGANTE/JALDESA

MARSABIT CENTRAL

SOLOLO

³

Percentage of Households with Improved and UnimprovedSanitation - Ward Level - Marsabit County

Legend

Improved SanitationUnimproved SanitationWater Bodies

County Boundary



Marsabit County Annex Tables

19


25. M

arsa

bit

Tabl

e 25.1

: Gen

der, A

ge g

roup

, Dem

ogra

phic

Indi

cato

rs an

d Ho

useh

olds

Size

by C

ount

y, Co

nstit

uenc

y and

War

ds

Coun

ty/C

onst

ituen

cy/W

ards

Gend

erAg

e gro

upDe

mog

raph

ic in

dica

tors

Pror

tion

of H

H Me

mbe

rs:

Tota

l Pop

Male

Fem

ale0-

5 yrs

0-14

yrs

10-1

8 yrs

15-3

4 yrs

15-6

4 yrs

65+ y

rsse

x Ra

tio

Tota

l de

pen-

danc

y Ra

tio

Child

de

pen-

danc

y Ra

tio

aged

de

pen-

danc

y ra

tio0-

3 4-

6 7+

to

tal

Keny

a

37

,919,6

47

18,78

7,698

19

,131,9

49

7,035

,670

16,34

6,414

8,2

93,20

7

13

,329,7

17

20,24

9,800

1,3

23,43

3

0.982

0.873

0.807

0.065

41

.5

38.4

20

.1

8,4

93,38

0

Rura

l

26

,075,1

95

12,86

9,034

13

,206,1

61

5,059

,515

12,02

4,773

6,1

34,73

0

8,303

,007

12,98

4,788

1,0

65,63

4

0.974

1.008

0.926

0.082

33

.2

41.3

25

.4

5,2

39,87

9

Urba

n

11

,844,4

52

5,918

,664

5,925

,788

1,976

,155

4,321

,641

2,158

,477

5,0

26,71

0

7,2

65,01

2

25

7,799

0.999

0.630

0.595

0.035

54

.8

33.7

11

.5

3,2

53,50

1

Marsa

bit C

ounty

288,4

13

148,8

14

139,5

99

54,85

9

13

5,104

74

,398

94

,192

141,2

36

12,07

3

1.066

1.042

0.957

0.085

28

.7

41.4

29

.9

53

,599

Moya

le C

onsti

tuenc

y

10

2,905

53,57

5

49,33

0

18

,898

50

,810

30,02

0

33,40

7

48,57

6

3,5

19

1.0

86

1.1

18

1.0

46

0.0

72

17.2

37

.7

45.1

1569

3

Butiy

e

17,44

7

9,192

8,255

2,908

7,965

5,119

6,1

43

8,8

11

67

1

1.114

0.980

0.904

0.076

16

.8

40.2

43

.1 27

58

Solol

o

8,219

4,170

4,049

1,381

3,727

2,434

2,7

56

4,0

70

42

2

1.030

1.019

0.916

0.104

22

.1

44.2

33

.7 14

55

Heillu

/Man

yatta

21

,116

11

,055

10

,061

3,6

42

10

,760

6,7

52

6,866

9,715

641

1.0

99

1.1

74

1.1

08

0.0

66

11.9

31

.8

56.4

2933

Golbo

28

,905

15

,137

13

,768

6,1

95

15

,807

8,1

41

8,174

12,29

1

807

1.0

99

1.3

52

1.2

86

0.0

66

11.0

36

.5

52.5

3807

Moya

le To

wnsh

ip

12,30

8

5,996

6,312

2,269

5,735

3,442

4,4

11

6,1

76

39

7

0.950

0.993

0.929

0.064

33

.0

31.6

35

.4 22

12

Uran

9,7

21

5,2

52

4,4

69

1,6

80

4,5

62

2,6

95

3,208

4,782

377

1.1

75

1.0

33

0.9

54

0.0

79

15.3

46

.0

38.7

1631

Obbu

5,1

89

2,7

73

2,4

16

82

3

2,254

1,437

1,8

49

2,7

31

20

4

1.148

0.900

0.825

0.075

18

.8

43.9

37

.2 89

7

North

Hor

r Co

nstitu

ency

74

,669

40

,892

33

,777

1

1,967

31,11

7

18

,324

25

,986

39

,664

3,888

1.211

0.883

0.785

0.098

31

.8

44.4

23

.8 15

131

Duka

na

18,59

8

10,54

7

8,051

2,642

6,978

4,668

7,1

91

10

,630

99

0

1.310

0.750

0.656

0.093

29

.4

46.9

23

.8 37

24

Maiko

na

19,13

2

10,08

1

9,051

3,068

7,849

4,501

6,3

44

10

,072

1,211

1.114

0.900

0.779

0.120

29

.3

47.2

23

.4 38

36

Turb

i

10,29

5

5,560

4,735

1,798

4,397

2,474

3,3

83

5,2

91

60

7

1.174

0.946

0.831

0.115

44

.9

41.4

13

.8 24

94

20



North

Hor

r

17,15

7

9,678

7,479

2,627

6,943

4,111

6,0

95

9,3

04

91

0

1.294

0.844

0.746

0.098

34

.3

44.6

21

.1 36

11

Illere

t

9,487

5,026

4,461

1,832

4,950

2,570

2,9

73

4,3

67

17

0

1.127

1.172

1.134

0.039

15

.9

35.7

48

.4 14

66

Saku

Con

stitue

ncy

45

,893

22

,802

23

,091

8,6

92

20

,973

11,58

6

15,04

9

22,90

5

2,0

15

0.9

87

1.0

04

0.9

16

0.0

88

35.1

40

.6

24.4

9482

Saga

nte/Ja

ldesa

16

,740

8,3

68

8,3

72

3,2

50

7,8

99

4,2

90

5,104

7,937

904

1.0

00

1.1

09

0.9

95

0.1

14

26.9

44

.0

29.2

3112

Kara

re

8,954

4,417

4,537

2,071

4,315

2,110

2,8

83

4,3

41

29

8

0.974

1.063

0.994

0.069

33

.3

46.1

20

.5 19

18

Marsa

bit C

entra

l

20,19

9

10,01

7

10,18

2

3,371

8,759

5,186

7,0

62

10

,627

81

3

0.984

0.901

0.824

0.077

41

.5

35.8

22

.6 44

52

Laisa

mis

Cons

tituen

cy

64,94

6

31,54

5

33,40

1

15

,302

32

,204

14,46

8

19,75

0

30,09

1

2,6

51

0.9

44

1.1

58

1.0

70

0.0

88

34.1

42

.8

23.0

1329

3

Loiya

ngala

ni

16,19

1

8,066

8,125

3,693

7,983

3,871

5,1

45

7,6

49

55

9

0.993

1.117

1.044

0.073

31

.0

45.1

23

.9 32

62

Karg

i/Sou

th Ho

rr

9,753

4,682

5,071

2,392

4,810

2,115

2,9

29

4,4

20

52

3

0.923

1.207

1.088

0.118

29

.6

41.8

28

.6 18

38

Korr/

Ngur

unit

21

,256

10

,304

10

,952

4,8

91

10

,491

4,7

19

6,439

9,923

842

0.9

41

1.1

42

1.0

57

0.0

85

28.5

42

.5

29.0

3939

Logo

logo

5,1

19

2,5

63

2,5

56

1,1

28

2,4

30

1,1

37

1,657

2,449

240

1.0

03

1.0

90

0.9

92

0.0

98

35.9

44

.1

20.0

1095

Laisa

mis

12

,627

5,9

30

6,6

97

3,1

98

6,4

90

2,6

26

3,580

5,650

487

0.8

85

1.2

35

1.1

49

0.0

86

46.5

41

.1

12.5

3159

21


Table 25.2: Employment by County, Constituency and Wards

County/Constituency/WardsWork for pay

Family Business

Family Agri-cultural Holding

Intern/Volunteer

Retired/Home-maker

Fulltime Student

Incapaci-tated No work


Kenya 23.7 13.1 32.0 1.1 9.2 12.8 0.5 7.7 20,249,800

Rural 15.6 11.2 43.5 1.0 8.8 13.0 0.5 6.3 12,984,788

Urban 38.1 16.4 11.4 1.3 9.9 12.2 0.3 10.2 7,265,012 Marsabit County 9.9 10.6 48.2 1.0 12.7 8.4 0.6 8.6 141,236

Moyale Constituency 13.6

9.4

27.3

1.1

23.3

14.3

1.0

9.9 48,576

Butiye 11.3

5.3

30.5

0.6

25.2

13.6

1.0

12.5 8,811

Sololo 19.9

8.2

26.7

3.3

9.6

19.0

0.6

12.6 4,070

Heillu/Manyatta 15.9

13.7

13.0

0.9

25.9

19.1

1.4

10.2 9,715

Golbo 7.5

7.0

38.4

0.8

29.4

9.7

1.0

6.1 12,291

Moyale Township 25.3

13.2

3.8

1.4

25.0

18.1

1.0

12.3 6,176

Uran 12.3

7.2

43.8

1.2

17.7

12.1

0.6

5.3 4,782

Obbu 7.3

16.1

44.1

0.7

6.3

8.4

0.4

16.7 2,731

North Horr Constituency 3.8

12.1

66.4

0.7

6.2

2.1

0.3

8.5 39,664

Dukana 2.2

6.0

75.3

0.4

5.3

1.3

0.2

9.3 10,630

Maikona 4.2

11.3

70.3

0.5

4.2

1.9

0.3

7.2 10,072

Turbi 4.0

35.1

46.8

0.7

3.1

2.4

0.2

7.7 5,291

North Horr 4.7

5.4

67.4

0.8

8.0

3.7

0.4

9.7 9,304

Illeret 4.9

14.9

57.1

1.7

12.9

0.5

0.4

7.6 4,367

Saku Constituency 18.6

9.5

36.8

1.7

12.8

10.5

0.7

9.6 22,905

Sagante/Jaldesa 13.5

5.1

43.7

1.7

18.6

9.6

0.6

7.3 7,937

Karare 8.3

5.6

67.3

0.8

4.0

8.6

0.4

5.0 4,341

Marsabit Central 26.5

14.3

19.2

2.1

12.0

12.0

0.8

13.1 10,627

Laisamis Constituency 5.3

11.5

66.5

0.9

4.0

5.5

0.3

6.0 30,091

Loiyangalani 5.7

12.7

68.8

0.4

1.9

4.4

0.4

5.8 7,649

Kargi/South Horr 4.3

17.6

58.6

0.6

8.0

6.7

0.3

4.0 4,420

Korr/Ngurunit 3.6

5.8

77.9

0.7

2.5

4.2

0.1

5.3 9,923

Logologo 4.7

7.7

54.9

1.6

5.3

11.0

0.3

14.7 2,449

Laisamis 8.7

16.8

54.8

1.7

6.0

6.0

0.5

5.6 5,650

22



Table 25.3: Employment and Education Levels by County, Constituency and Wards

County /constituency/WardsEducation Totallevel

Work for pay

Family Business

Family Agri-cultural Holding

Intern/Volun-teer

Retired/Home-maker

Fulltime Student



Kenya Total 23.7 13.1 32.0 1.1 9.2 12.8 0.5 7.7 20,249,800

Kenya None 11.1 14.0 44.4 1.7 14.7 0.8 1.2 12.1 3,154,356

Kenya Primary 20.7 12.6 37.3 0.8 9.6 12.1 0.4 6.5 9,528,270

Kenya Secondary+ 32.7 13.3 20.2 1.2 6.6 18.6 0.2 7.3 7,567,174

Rural Total 15.6 11.2 43.5 1.0 8.8 13.0 0.5 6.3 12,984,788

Rural None 8.5 13.6 50.0 1.4 13.9 0.7 1.2 10.7 2,614,951

Rural Primary 15.5 10.8 45.9 0.8 8.4 13.2 0.5 5.0 6,785,745

Rural Secondary+ 21.0 10.1 34.3 1.0 5.9 21.9 0.3 5.5 3,584,092

Urban Total 38.1 16.4 11.4 1.3 9.9 12.2 0.3 10.2 7,265,012

Urban None 23.5 15.8 17.1 3.1 18.7 1.5 1.6 18.8 539,405

Urban Primary 33.6 16.9 16.0 1.0 12.3 9.5 0.4 10.2 2,742,525

Urban Secondary+ 43.2 16.1 7.5 1.3 7.1 15.6 0.2 9.0 3,983,082

Marsabit Total 9.9 10.6 48.2 1.0 12.7 8.4 0.6 8.6 141,236

Marsabit None 6.0 11.1 58.5 0.9 14.6 0.4 0.7 7.8 100,796

Marsabit Primary 12.5 10.5 27.1 1.1 10.3 27.7 0.4 10.5 25,272

Marsabit Secondary+ 31.5 7.6 14.5 1.8 3.7 29.4 0.3 11.3 15,168

Moyale Constituency Total 13.6

9.4

27.3

1.1

23.3

14.3

1.0

9.9

48,576

Moyale Constituency None 10.9

10.1

35.8

1.1

30.3

1.0

1.3

9.6

30,964

Moyale Constituency Primary 12.4

9.4

14.8

1.0

14.9

37.0

0.5

10.0

11,025

Moyale Constituency Secondary+ 28.6

6.5

8.7

1.4

4.3

38.9

0.4

11.4

6,587

Butiye Wards Total 11.3

5.3

30.5

0.6

25.2

13.6

1.0

12.5

8,811

Butiye Wards None 9.4

5.7

39.1

0.6

31.5

0.2

1.1

12.4

5,822

Butiye Wards Primary 12.1

5.8

15.7

0.8

18.2

34.5

0.9

12.0

1,837

Butiye Wards Secondary+ 19.8

2.4

10.3

0.8

4.8

47.7

0.4

13.7

1,152

Sololo Wards Total 19.9

8.2

26.7

3.3

9.6

19.0

0.6

12.6

4,070

Sololo Wards None 19.5

10.2

37.1

5.1

14.9

0.2

0.9

12.0

2,146

Sololo Wards Primary 11.6

7.4

20.0

1.4

5.2

40.8

0.4

13.2

1,070

Sololo Wards Secondary+ 31.2

4.2

8.9

1.2

1.6

39.0

0.4

13.6

854

23


Heillu/Manyatta Wards Total 15.9

13.7

13.0

0.9

25.9

19.1

1.4

10.2

9,715

Heillu/Manyatta Wards None 14.7

16.3

19.8

0.8

34.7

1.2

1.8

10.9

5,748

Heillu/Manyatta Wards Primary 15.9

11.8

3.9

0.9

16.8

41.1

0.8

8.8

2,740

Heillu/Manyatta Wards Secondary+ 22.0

5.9

1.6

1.1

5.1

53.7

0.8

9.9

1,227

Golbo Wards Total 7.5

7.0

38.4

0.8

29.4

9.7

1.0

6.1

12,291

Golbo Wards None 6.3

6.4

44.5

0.5

34.7

1.3

1.2

5.1

9,459

Golbo Wards Primary 8.6

9.2

18.4

1.4

14.4

38.3

0.3

9.4

2,120

Golbo Wards Secondary+ 21.2

8.3

17.1

2.5

3.9

36.7 -

10.3

712

Moyale Township Wards Total 25.3

13.2

3.8

1.4

25.0

18.1

1.0

12.3

6,176

Moyale Township Wards None 20.3

14.8

4.3

1.5

40.1

3.4

1.8

13.9

2,676

Moyale Township Wards Primary 16.8

13.0

4.2

1.1

21.0

31.0

0.3

12.7

1,753

Moyale Township Wards Secondary+ 41.5

11.1

2.5

1.6

6.0

27.6

0.3

9.6

1,747

Uran Wards Total 12.3

7.2

43.8

1.2

17.7

12.1

0.6

5.3

4,782

Uran Wards None 9.9

7.8

53.0

1.4

22.9

0.1

0.8

4.3

3,191

Uran Wards Primary 8.5

6.8

31.7

0.8

9.4

37.6

0.4

4.8

1,024

Uran Wards Secondary+ 32.5

4.2

13.4

1.1

3.0

33.7

0.4

11.8

567

Obbu Wards Total 7.3

16.1

44.1

0.7

6.3

8.4

0.4

16.7

2,731

Obbu Wards None 6.1

20.5

44.9

0.4

7.8

0.1

0.6

19.8

1,922

Obbu Wards Primary 4.8

6.9

47.2

0.8

4.4

29.7

0.2

6.0

481

Obbu Wards Secondary+ 17.7

4.0

35.1

1.8

0.6

26.2 -

14.6

328

North Horr Constituency Total 3.8

12.1

66.4

0.7

6.2

2.1

0.3

8.5

39,664

North Horr Constituency None 1.9

12.2

70.9

0.7

6.8

0.0

0.3

7.3

34,120

North Horr Constituency Primary 7.5

12.8

45.8

0.6

3.5

14.4

0.2

15.3

3,531

North Horr Constituency Secondary+ 31.0

8.2

26.2

1.4

1.2

15.2

0.2

16.7

2,013

Dukana Wards Total 2.2

6.0

75.3

0.4

5.3

1.3

0.2

9.3

10,630

Dukana Wards None 1.0

5.8

79.0

0.4

5.8

0.0

0.2

7.8

9,704

Dukana Wards Primary 5.4

8.2

45.0

0.3

0.5

15.5 -

25.1

625

Dukana Wards Secondary+ 36.2

7.3

17.3

0.3

0.3

13.3 -

25.3

301

Maikona Wards Total 4.2

11.3

70.3

0.5

4.2

1.9

0.3

7.2

10,072

Maikona Wards None 1.5

11.3

77.0

0.3

4.5

0.0

0.3

5.0

8,054

Maikona Wards Primary 6.2

13.3

50.3

0.5

4.5

9.0

0.3

15.9

1,252

24



Maikona Wards Secondary+ 29.5

8.0

32.9

2.1

1.2

9.8

0.1

16.5

766

Turbi Wards Total 4.0

35.1

46.8

0.7

3.1

2.4

0.2

7.7

5,291

Turbi Wards None 1.7

38.5

47.6

0.8

3.6

0.0

0.2

7.7

4,353

Turbi Wards Primary 6.1

23.3

49.9

0.3

1.0

12.6

0.3

6.5

619

Turbi Wards Secondary+ 31.7

11.9

29.5

0.3

0.9

15.4 -

10.3

319

North Horr Wards Total 4.7

5.4

67.4

0.8

8.0

3.7

0.4

9.7

9,304

North Horr Wards None 2.5

5.2

73.7

0.8

8.6

0.0

0.4

8.8

7,890

North Horr Wards Primary 9.7

7.4

39.0

1.0

5.9

23.3

0.2

13.4

879

North Horr Wards Secondary+ 29.0

6.2

19.8

0.9

2.1

25.2

0.4

16.5

535

Illeret Wards Total 4.9

14.9

57.1

1.7

12.9

0.5

0.4

7.6

4,367

Illeret Wards None 3.6

14.9

58.7

1.7

13.6

0.0

0.4

7.1

4,119

Illeret Wards Primary 19.9

16.0

34.0

1.9

3.2

9.0 -

16.0

156

Illeret Wards Secondary+ 35.9

10.9

26.1

5.4

-

6.5 -

15.2

92

Saku Constituency Total 18.6

9.5

36.8

1.7

12.8

10.5

0.7

9.6

22,905

Saku Constituency None 12.3

9.2

50.1

1.6

17.3

0.1

0.9

8.5

11,037

Saku Constituency Primary 16.9

10.6

30.3

1.2

10.9

18.9

0.5

10.7

7,198

Saku Constituency Secondary+ 36.0

8.3

15.2

2.6

5.1

22.1

0.3

10.3

4,670

Sagante/Jaldesa Wards Total 13.5

5.1

43.7

1.7

18.6

9.6

0.6

7.3

7,937

Sagante/Jaldesa Wards None 11.5

5.3

50.7

1.8

24.2

0.0

0.7

5.8

4,565

Sagante/Jaldesa Wards Primary 13.9

4.9

36.0

1.2

12.9

21.6

0.5

9.0

2,380

Sagante/Jaldesa Wards Secondary+ 22.3

4.5

29.6

2.1

6.9

24.4

0.2

10.0

992

Karare Wards Total 8.3

5.6

67.3

0.8

4.0

8.6

0.4

5.0

4,341

Karare Wards None 5.1

5.9

77.2

0.8

4.8

0.1

0.5

5.5

2,828

Karare Wards Primary 9.4

5.4

53.1

0.4

2.3

25.4

0.3

3.8

1,121

Karare Wards Secondary+ 28.3

3.8

36.0

2.3

2.6

22.5

0.3

4.3

392

Marsabit Central Wards Total 26.5

14.3

19.2

2.1

12.0

12.0

0.8

13.1

10,627

Marsabit Central Wards None 19.0

16.8

28.2

1.9

18.3

0.3

1.5

14.1

3,644

Marsabit Central Wards Primary 21.1

15.8

19.8

1.5

12.1

15.2

0.6

13.9

3,697

Marsabit Central Wards Secondary+ 41.1

10.0

8.4

2.8

4.9

21.4

0.3

11.1

3,286

Laisamis Constituency Total 5.3

11.5

66.5

0.9

4.0

5.5

0.3

6.0

30,091

Laisamis Constituency None 2.8

11.7

73.8

0.7

4.6

0.2

0.3

5.9

24,675

25


Laisamis Constituency Primary 8.5

11.3

40.6

1.1

1.8

30.0

0.2

6.5

3,518

Laisamis Constituency Secondary+ 30.7

9.4

20.6

2.0

0.5

29.2 -

7.6

1,898

Loiyangalani Wards Total 5.7

12.7

68.8

0.4

1.9

4.4

0.4

5.8

7,649

Loiyangalani Wards None 3.6

12.7

74.8

0.4

2.1

0.1

0.4

5.9

6,220

Loiyangalani Wards Primary 8.9

13.2

48.4

0.1

1.4

23.6

0.2

4.3

1,017

Loiyangalani Wards Secondary+ 28.4

12.6

28.2

1.5

0.2

20.4 -

8.7

412

Kargi/South Horr Wards Total 4.3

17.6

58.6

0.6

8.0

6.7

0.3

4.0

4,420

Kargi/South Horr Wards None 1.5

18.7

66.1

0.5

9.2

0.1

0.3

3.7

3,580

Kargi/South Horr Wards Primary 6.1

15.1

32.9

1.3

3.2

35.7

0.4

5.4

538

Kargi/South Horr Wards Secondary+ 34.1

8.9

14.9

1.7

2.0

33.4 -

5.0

302

Korr/Ngurunit Wards Total 3.6

5.8

77.9

0.7

2.5

4.2

0.1

5.3

9,923

Korr/Ngurunit Wards None 2.0

5.7

83.4

0.7

2.7

0.2

0.2

5.2

8,637

Korr/Ngurunit Wards Primary 7.4

6.5

46.9

1.0

1.4

31.8

0.1

4.9

842

Korr/Ngurunit Wards Secondary+ 27.0

6.8

28.2

1.1

-

29.3 -

7.7

444

Logologo Wards Total 4.7

7.7

54.9

1.6

5.3

11.0

0.3

14.7

2,449

Logologo Wards None 2.9

8.1

63.1

1.3

6.6

0.6

0.2

17.2

1,806

Logologo Wards Primary 6.3

5.4

38.2

2.3

2.1

38.0

0.7

7.0

429

Logologo Wards Secondary+ 16.8

8.4

19.2

1.9

0.5

44.4 -

8.9

214

Laisamis Wards Total 8.7

16.8

54.8

1.7

6.0

6.0

0.5

5.6

5,650

Laisamis Wards None 4.5

17.8

64.0

1.4

7.4

0.1

0.6

4.3

4,432

Laisamis Wards Primary 12.6

15.3

28.8

1.9

1.6

27.8 -

12.1

692

Laisamis Wards Secondary+ 39.4

9.9

12.0

3.4

0.4

27.4 -

7.6

526

Table 25.4: Employment and Education Levels in Male Headed Household by County, Constituency and Wards

County, Constituency and Wards

Education Level reached

Work for Pay

Family Business

Family Agri-cultural holding

Internal/ Volunteer

Retired/ Home-maker

Fulltime Student


Population (15-64)

Kenya National Total 25.5

13.5

31.6

1.1

9.0

11.4

0.4

7.5 14,757,992

Kenya National None 11.4

14.3

44.2

1.6

13.9

0.9

1.0

12.6 2,183,284

Kenya National Primary 22.2

12.9

37.3

0.8

9.4

10.6

0.4

6.4 6,939,667

Kenya National Secondary+ 35.0

13.8

19.8

1.1

6.5

16.5

0.2

7.0 5,635,041

Rural Rural Total 16.8

11.6

43.9

1.0

8.3

11.7

0.5

6.3 9,262,744

26



Rural Rural None 8.6

14.1

49.8

1.4

13.0

0.8

1.0

11.4 1,823,487

Rural Rural Primary 16.5

11.2

46.7

0.8

8.0

11.6

0.4

4.9 4,862,291

Rural Rural Secondary+ 23.1

10.6

34.7

1.0

5.5

19.6

0.2

5.3 2,576,966

Urban Urban Total 40.2

16.6

10.9

1.3

10.1

10.9

0.3

9.7 5,495,248

Urban Urban None 25.8

15.5

16.1

3.0

18.2

1.4

1.3

18.7 359,797

Urban Urban Primary 35.6

16.9

15.4

1.0

12.8

8.1

0.3

9.9 2,077,376

Urban Urban Secondary+ 45.1

16.6

7.3

1.2

7.4

13.8

0.1

8.5 3,058,075

Marsabit Total 10.6

11.0

49.8

0.9 11.9

7.1

0.5

8.0 97,984

Marsabit None 6.1

11.3

60.1

0.8

13.5

0.3

0.6

7.3 71,404

Marsabit Primary 14.6

11.2

27.4

1.1

10.3

25.1

0.4

9.9 16,285

Marsabit Secondary+ 35.9

8.5

14.0

1.8

3.6

25.8

0.3

10.2 10,295

Moyale Constituency Total 14.4

9.8

30.3

1.0

22.0

12.2

0.9

9.5 34,464

Moyale Constituency None 10.7

10.1

39.2

0.9

27.9

0.8

1.1

9.3 22,650

Moyale Constituency Primary 14.3

10.7

15.7

1.1

14.4

33.8

0.5

9.4 7,274

Moyale Constituency Secondary+ 32.8

7.3

9.0

1.5

4.5

34.4

0.3

10.3 4,540

Butiye Ward Total 11.5

5.2

35.0

0.5

23.5

11.8

0.8

11.6 6,351

Butiye Ward None 9.0

5.2

43.8

0.4

28.6

0.1

0.9

12.0 4,397

Butiye Ward Primary 13.9

6.7

17.8

0.7

16.9

32.9

0.7

10.5 1,177

Butiye Ward Secondary+ 22.1

2.7

11.6

0.9

5.0

45.9

0.4

11.3 777

Sololo Ward Total 22.0

9.3

27.3

3.0

8.9

16.8

0.5

12.2 2,630

Sololo Ward None 20.5

11.2

38.2

4.4

13.4

-

0.5

11.8 1,393

Sololo Ward Primary 13.3

9.0

20.2

1.8

5.4

35.9

0.4

14.0 679

Sololo Ward Secondary+ 36.4

4.8

8.8

1.1

2.0

35.7

0.4

10.9 558

Heillu/Manyatta Ward Total 17.7

13.8

13.1

0.9

25.8

17.8

1.4

9.4 6,627

Heillu/Manyatta Ward None 15.9

15.8

19.9

0.7

34.6

1.1

1.8

10.3 3,944

Heillu/Manyatta Ward Primary 17.9

13.1

4.0

1.1

16.9

38.8

0.9

7.3 1,843

Heillu/Manyatta Ward Secondary+ 25.5

6.2

1.5

1.3

4.3

50.5

0.6

10.1 840

Golbo Ward Total 7.8

7.5

42.0

0.8

27.4

7.7

1.0

5.9 9,454

Golbo Ward None 6.3

6.7

47.9

0.5

31.5

1.1

1.2

4.9 7,474

Golbo Ward Primary 10.4

10.6

20.3

1.4

14.0

33.6

0.3

9.5 1,468

Golbo Ward Secondary+ 23.2

10.2

19.1

2.7

4.9

29.5 -

10.4 512

27


Moyale Township Ward Total 29.7

14.5

3.9

1.3

22.4

15.5

0.9

11.8 3,942

Moyale Township Ward None 22.3

16.2

4.8

1.3

37.3

2.9

1.6

13.7 1,572

Moyale Township Ward Primary 19.9

15.0

4.2

1.1

19.5

27.2

0.4

12.8 1,128

Moyale Township Ward Secondary+ 47.8

12.1

2.6

1.5

6.2

20.9

0.4

8.5 1,242

Uran Ward Total 12.4

6.9

46.7

1.0

17.0

10.4

0.5

5.1 3,390

Uran Ward None 9.0

7.4

56.1

1.2

21.3

0.1

0.6

4.3 2,313

Uran Ward Primary 9.4

6.6

34.0

0.6

9.8

35.0

0.4

4.1 682

Uran Ward Secondary+ 37.7

4.3

13.9

0.8

3.8

27.8 -

11.6 395

Obbu Ward Total 6.9

18.6

43.6

0.8

4.7

6.7

0.2

18.6 2,070

Obbu Ward None 5.6

22.3

44.1

0.4

5.4

0.1

0.3

21.8 1,557

Obbu Ward Primary 5.4

8.8

49.2

1.3

3.7

25.9 -

5.7 297

Obbu Ward Secondary+ 18.1

5.1

32.4

2.8

0.9

28.2 -

12.5 216

North Horr Constit-uency Total

3.8

12.4

68.2

0.7

5.6

1.8

0.2

7.2 30,483

North Horr Constit-uency None

1.8

12.6

72.4

0.6

6.1

0.0

0.3

6.3 26,665

North Horr Constit-uency Primary

8.3

12.6

47.1

0.8

3.3

14.3

0.2

13.6 2,390

North Horr Constit-uency Secondary+

33.7

8.5

25.9

1.3

1.1

14.9

0.2

14.4 1,428

Dukana Ward Total 2.3

6.0

78.1

0.4

4.7

1.2

0.1

7.2 8,252

Dukana Ward None 1.0

5.8

81.5

0.4

5.1

0.0

0.1

6.1 7,601

Dukana Ward Primary 6.5

8.3

49.8

0.2

0.2

15.0 -

20.0 434

Dukana Ward Secondary+ 38.7

7.8

17.5

0.5

0.5

14.3 -

20.7 217

Maikona Ward Total 4.3

11.6

71.1

0.4

3.9

1.9

0.3

6.5 7,742

Maikona Ward None 1.6

12.0

77.1

0.3

4.1

0.0

0.3

4.6 6,346

Maikona Ward Primary 6.9

12.1

50.6

0.6

4.5

9.7

0.2

15.4 826

Maikona Ward Secondary+ 30.2

6.7

33.7

1.8

0.9

11.6

0.2

15.1 570

Turbi Ward Total 3.8

38.9

45.1

0.6

2.1

2.0

0.1

7.2 3,911

Turbi Ward None 1.6

42.4

45.6

0.7

2.4

-

0.1

7.3 3,275

Turbi Ward Primary 5.8

23.7

50.5

0.5

0.5

12.9 -

6.2 434

Turbi Ward Secondary+ 36.1

16.3

26.2

0.5

1.0

11.4 -

8.4 202

North Horr Ward Total 4.6

4.7

70.4

0.8

7.8

3.1

0.4

8.3 7,136

North Horr Ward None 2.3

4.4

76.3

0.7

8.3

0.0

0.3

7.7 6,173

North Horr Ward Primary 10.4

7.0

40.6

1.4

5.6

22.8

0.3

12.1 589

28



North Horr Ward Secondary+ 33.4

6.4

19.8

1.1

1.9

23.3

0.5

13.6 374

Illeret Ward Total 5.0

15.2

59.9

1.6 11.1

0.3

0.4

6.3 3,442

Illeret Ward None 3.6

15.1

61.7

1.6 11.5

-

0.4

6.1 3,270

Illeret Ward Primary 25.2

18.7

31.8

1.9

4.7

5.6 -

12.1 107

Illeret Ward Secondary+ 41.5

15.4

20.0

4.6 -

9.2 -

9.2 65

Saku Constituency Total 21.7

9.7

35.9

1.6

12.0

9.1

0.6

9.2 14,964

Saku Constituency None 13.9

9.0

50.3

1.6

16.0

0.1

0.9

8.2 7,173

Saku Constituency Primary 20.2

11.1

29.0

1.2 11.1

16.3

0.5

10.6 4,603

Saku Constituency Secondary+ 41.6

9.3

13.5

2.2

4.5

19.1

0.3

9.5 3,188

Sagante/Jaldesa Ward Total 15.4

5.2

44.9

1.7

17.4

8.4

0.5

6.6 5,178

Sagante/Jaldesa Ward None 12.7

5.4

53.4

1.9

21.2

-

0.6

4.7 3,013

Sagante/Jaldesa Ward Primary 16.8

4.7

35.0

1.2

14.3

18.7

0.5

8.9 1,541

Sagante/Jaldesa Ward Secondary+ 25.2

5.1

28.0

2.1

6.3

23.2

0.3

9.8 624

Karare Ward Total 10.3

5.6

66.8

0.8

4.0

7.0

0.3

5.1 2,660

Karare Ward None 6.0

6.2

76.2

0.9

4.8

0.1

0.3

5.5 1,805

Karare Ward Primary 12.4

4.6

52.4

0.5

2.6

23.5

0.2

3.9 614

Karare Ward Secondary+ 37.3

3.7

33.2

1.2

2.1

17.4 -

5.0 241

Marsabit Central Ward Total 30.6

14.6

17.9

1.8 11.1

10.5

0.8

12.7 7,126

Marsabit Central Ward None 21.4

15.8

26.6

1.7

17.8

0.3

1.6

14.8 2,355

Marsabit Central Ward Primary 24.3

16.8

19.4

1.4 11.2

13.0

0.6

13.3 2,448

Marsabit Central Ward Secondary+ 46.4

11.1

7.5

2.3

4.2

18.2

0.4

9.9 2,323

Laisamis Constituency Total 5.8

11.9

67.6

0.8

3.2

4.6

0.3

5.8 18,073

Laisamis Constituency None 3.0

12.0

74.6

0.6

3.7

0.1

0.3

5.7 14,916

Laisamis Constituency Primary 10.0

11.9

42.4

1.2

1.5

26.5

0.3

6.1 2,018

Laisamis Constituency Secondary+ 34.8

11.0

20.5

2.6

0.4

24.1 -

6.5 1,139

Loiyangalani Ward Total 6.1

14.2

68.1

0.4

1.5

3.7

0.3

5.6 4,941

Loiyangalani Ward None 3.8

14.1

73.5

0.3

1.7

0.1

0.3

6.0 4,017

Loiyangalani Ward Primary 10.2

14.3

51.1

0.2

0.6

19.8

0.3

3.5 665

Loiyangalani Ward Secondary+ 30.5

15.4

28.2

1.9

0.4

18.1 -

5.4 259

Kargi/South Horr Ward Total 5.6

16.6

61.7

0.6

6.3

5.6

0.4

3.2 2,403

Kargi/South Horr Ward None 1.3

17.3

70.5

0.4

7.3

0.1

0.4

2.6 1,934

29


Kargi/South Horr Ward Primary 9.5

15.5

32.7

1.4

2.8

32.0

0.4

5.6 284

Kargi/South Horr Ward Secondary+ 44.3

10.8

14.1

1.6

1.1

23.2 -

4.9 185

Korr/Ngurunit Ward Total 4.0

6.0

77.7

0.6

2.2

4.0

0.2

5.3 6,225

Korr/Ngurunit Ward None 2.3

6.0

83.2

0.5

2.3

0.1

0.2

5.4 5,435

Korr/Ngurunit Ward Primary 7.3

5.1

46.9

0.8

1.8

32.9

0.2

5.0 505

Korr/Ngurunit Ward Secondary+ 31.6

7.7

26.0

2.8 -

26.3 -

5.6 285

Logologo Ward Total 4.1

9.1

57.5

1.4

4.8

8.2

0.2

14.6 1,389

Logologo Ward None 2.0

9.8

64.3

1.0

5.9

0.2

0.1

16.8 1,072

Logologo Ward Primary 7.3

5.4

41.0

3.4

2.0

33.2

1.0

6.8 205

Logologo Ward Secondary+ 18.8

8.9

23.2

1.8 -

39.3 -

8.0 112

Laisamis Ward Total 9.7

17.8

55.9

1.6

4.9

4.7

0.5

4.9 3,115

Laisamis Ward None 5.0

18.6

65.3

1.2

5.9

0.1

0.7

3.3 2,458

Laisamis Ward Primary 15.3

17.8

28.1

2.5

1.7

21.7 -

12.8 359

Laisamis Ward Secondary+ 41.6

11.1

11.7

4.0

0.7

22.1 -

8.7 298

Table 25.5: Employment and Education Levels in Female Headed Households by County, Constituency and Wards

County, Constituency and Wards

Education Level reached

Work for Pay

Family Business

Family Agri-cultural holding

Internal/ Volunteer

Retired/Home-maker

Fulltime Student

Incapaci-tated

No work

Popula-tion(15-64)

Kenya National Total 18.87

11.91

32.74

1.20

9.85

16.66

0.69

8.08 5,518,645

Kenya National None 10.34

13.04

44.55

1.90

16.45

0.80

1.76

11.17 974,824

Kenya National Primary 16.74

11.75

37.10

0.89

9.82

16.23

0.59

6.89 2,589,877

Kenya National Secondary+ 25.95

11.57

21.07

1.27

6.59

25.16

0.28

8.11 1,953,944

Rural Rural Total 31.53

15.66

12.80

1.54

9.33

16.99

0.54

11.60 1,781,078

Rural Rural None 8.36

12.26

50.31

1.60

15.77

0.59

1.67

9.44 794,993

Rural Rural Primary 13.02

9.90

43.79

0.81

9.49

17.03

0.60

5.36 1,924,111

Rural Rural Secondary+ 15.97

8.87

33.03

1.06

6.80

27.95

0.34

5.98 1,018,463

Urban Urban Total 12.83

10.12

42.24

1.04

10.09

16.51

0.76

6.40 3,737,567

Urban Urban None 19.09

16.50

19.04

3.22

19.45

1.70

2.18

18.83 179,831

Urban Urban Primary 27.49

17.07

17.79

1.13

10.76

13.93

0.55

11.29 665,766

Urban Urban Secondary+ 36.81

14.50

8.06

1.51

6.36

22.11

0.22

10.43 935,481

30



Marsabit Total 8.4% 9.8% 44.3% 1.2% 14.4% 11.2% .7% 9.9% 43310

Marsabit None 5.8% 10.7% 54.6% 1.2% 17.4% .5% .8% 8.9% 29379

Marsabit Primary 8.6% 9.1% 26.6% 1.0% 10.4% 32.5% .4% 11.4% 8988Marsabit Secondary+ 23.3% 5.7% 15.4% 2.0% 3.8% 36.2% .3% 13.4% 4943

Moyale Constituency Total 11.8% 8.5% 20.2% 1.4% 26.4% 19.4% 1.2% 11.1% 14098

Moyale Constituency None 11.5% 10.2% 26.5% 1.6% 36.7% 1.5% 1.6% 10.4% 8312

Moyale Constituency Primary 8.7% 6.8% 12.9% .9% 15.7% 43.1% .6% 11.2% 3747

Moyale Constituency Secondary+ 19.1% 4.7% 8.0% 1.1% 3.8% 48.8% .5% 13.9% 2039

Butiye Ward Total 10.7% 5.6% 18.8% 1.0% 29.6% 18.0% 1.5% 14.8% 2454

Butiye Ward None 10.5% 7.2% 24.7% 1.2% 40.4% .4% 1.8% 13.8% 1424

Butiye Ward Primary 8.8% 4.2% 12.1% .9% 20.5% 37.3% 1.2% 14.9% 659

Butiye Ward Secondary+ 15.1% 1.9% 7.8% .5% 4.3% 51.2% .5% 18.6% 371

Sololo Ward Total 16.0% 6.3% 25.6% 3.9% 10.8% 23.1% 1.0% 13.5% 1440

Sololo Ward None 17.7% 8.4% 35.2% 6.5% 17.7% .7% 1.6% 12.4% 753

Sololo Ward Primary 8.7% 4.6% 19.7% .8% 4.9% 49.4% .3% 11.8% 391

Sololo Ward Secondary+ 21.3% 3.0% 9.1% 1.4% 1.0% 45.3% .3% 18.6% 296

Heillu/Manyatta Ward Total 12.3% 13.4% 12.7% .8% 26.1% 21.7% 1.4% 11.7% 3089

Heillu/Manyatta Ward None 12.1% 17.2% 19.4% 1.1% 35.0% 1.4% 1.7% 12.1% 1805

Heillu/Manyatta Ward Primary 11.7% 9.3% 3.7% .6% 16.5% 45.7% .8% 11.8% 897

Heillu/Manyatta Ward Secondary+ 14.5% 5.2% 1.8% .5% 6.7% 60.7% 1.3% 9.3% 387

Golbo Ward Total 6.6% 5.2% 26.2% 1.0% 36.3% 16.7% 1.1% 7.0% 2837

Golbo Ward None 6.2% 5.2% 31.6% .7% 46.6% 2.2% 1.4% 6.0% 1985

Golbo Ward Primary 4.6% 5.8% 14.1% 1.5% 15.5% 48.9% .3% 9.2% 652

Golbo Ward Secondary+ 16.0% 3.5% 12.0% 2.0% 1.5% 55.0% 0.0% 10.0% 200

Moyale Township Ward Total 17.5% 10.9% 3.5% 1.5% 29.6% 22.6% 1.1% 13.3% 2230

Moyale Township Ward None 17.4% 12.9% 3.7% 1.6% 44.0% 4.0% 2.2% 14.2% 1104

Moyale Township Ward Primary 11.1% 9.1% 4.2% 1.1% 23.8% 38.0% .2% 12.5% 623

Moyale Township Ward Secondary+ 25.6% 8.7% 2.0% 1.6% 5.4% 44.3% 0.0% 12.3% 503

Uran Ward Total 11.8% 7.9% 36.5% 1.6% 19.4% 16.1% .9% 5.8% 1387

Uran Ward None 12.1% 8.8% 44.7% 1.7% 27.2% 0.0% 1.1% 4.3% 876

Uran Ward Primary 6.7% 7.3% 27.3% 1.2% 8.5% 42.5% .3% 6.2% 341

Uran Ward Secondary+ 20.6% 4.1% 12.4% 1.8% 1.2% 46.5% 1.2% 12.4% 170

Obbu Ward Total 8.6% 8.3% 45.5% .3% 11.3% 13.8% 1.1% 11.0% 661

Obbu Ward None 8.5% 12.6% 47.9% .5% 17.8% 0.0% 1.6% 11.0% 365

Obbu Ward Primary 3.8% 3.8% 44.0% 0.0% 5.4% 35.9% .5% 6.5% 184

Obbu Ward Secondary+ 17.0% 1.8% 40.2% 0.0% 0.0% 22.3% 0.0% 18.8% 112

North Horr Constituency Total 3.9% 11.0% 60.1% .8% 8.2% 2.9% .4% 12.6% 9177

North Horr Constituency None 2.0% 11.0% 65.4% .8% 9.4% .0% .5% 10.9% 7449

North Horr Constituency Primary 5.9% 13.3% 42.9% .4% 3.9% 14.6% .4% 18.7% 1142

North Horr Constituency Secondary+ 24.4% 7.3% 27.0% 1.5% 1.5% 15.7% 0.0% 22.5% 586

Dukana Ward Total 2.0% 6.0% 65.5% .3% 7.5% 1.7% .5% 16.5% 2375

Dukana Ward None .8% 5.8% 70.3% .3% 8.4% 0.0% .5% 13.8% 2100

Dukana Ward Primary 3.1% 7.9% 34.0% .5% 1.0% 16.8% 0.0% 36.6% 191

Dukana Ward Secondary+ 29.8% 6.0% 16.7% 0.0% 0.0% 10.7% 0.0% 36.9% 84

Maikona Ward Total 4.1% 10.4% 67.8% .6% 5.4% 1.8% .4% 9.5% 2330

Maikona Ward None 1.2% 9.0% 76.6% .4% 6.0% 0.0% .4% 6.4% 1708

Maikona Ward Primary 4.7% 15.7% 49.8% .2% 4.5% 7.7% .5% 16.9% 426

Maikona Ward Secondary+ 27.6% 11.7% 30.6% 3.1% 2.0% 4.6% 0.0% 20.4% 196

31


Turbi Ward Total 4.5% 24.2% 51.5% .9% 5.9% 3.6% .4% 9.1% 1381

Turbi Ward None 1.9% 26.7% 53.8% 1.1% 7.1% .1% .4% 8.9% 1078

Turbi Ward Primary 7.0% 22.0% 48.4% 0.0% 2.2% 12.4% 1.1% 7.0% 186

Turbi Ward Secondary+ 23.9% 4.3% 35.0% 0.0% .9% 22.2% 0.0% 13.7% 117

North Horr Ward Total 5.1% 7.7% 57.3% 1.0% 8.7% 5.5% .6% 14.1% 2169

North Horr Ward None 3.3% 7.8% 64.5% 1.2% 9.6% .1% .7% 12.9% 1717

North Horr Ward Primary 8.3% 8.3% 35.9% .3% 6.6% 24.5% 0.0% 16.2% 290

North Horr Ward Secondary+ 18.5% 6.2% 19.8% .6% 2.5% 29.6% 0.0% 22.8% 162

Illeret Ward Total 4.4% 13.7% 46.3% 2.2% 19.6% 1.0% .3% 12.5% 922

Illeret Ward None 3.7% 14.3% 46.9% 2.0% 21.4% .1% .4% 11.2% 846

Illeret Ward Primary 8.2% 10.2% 38.8% 2.0% 0.0% 16.3% 0.0% 24.5% 49

Illeret Ward Secondary+ 22.2% 0.0% 40.7% 7.4% 0.0% 0.0% 0.0% 29.6% 27

Saku Constituency Total 12.6% 9.0% 38.3% 1.9% 14.2% 13.2% .7% 10.1% 7944

Saku Constituency None 9.4% 9.7% 49.6% 1.6% 19.6% .2% 1.0% 8.9% 3863

Saku Constituency Primary 11.0% 9.6% 32.6% 1.3% 10.5% 23.8% .5% 10.9% 2600

Saku Constituency Secondary+ 24.0% 6.0% 19.0% 3.6% 6.5% 28.6% .2% 12.0% 1481

Sagante/Jaldesa Ward Total 10.0% 4.9% 41.4% 1.7% 21.0% 11.8% .6% 8.6% 2759

Sagante/Jaldesa Ward None 9.0% 5.0% 45.5% 1.8% 29.8% .1% .9% 7.9% 1552

Sagante/Jaldesa Ward Primary 8.6% 5.4% 37.9% 1.2% 10.5% 26.9% .4% 9.2% 839

Sagante/Jaldesa Ward Secondary+ 17.4% 3.5% 32.3% 2.2% 7.9% 26.4% 0.0% 10.3% 368

Karare Ward Total 5.2% 5.5% 67.9% .8% 3.9% 11.2% .7% 4.8% 1681

Karare Ward None 3.6% 5.4% 79.0% .7% 4.9% .1% .9% 5.5% 1023

Karare Ward Primary 5.7% 6.3% 53.8% .2% 2.0% 27.8% .4% 3.7% 507

Karare Ward Secondary+ 13.9% 4.0% 40.4% 4.0% 3.3% 30.5% .7% 3.3% 151

Marsabit Central Ward Total 18.3% 13.8% 21.7% 2.6% 13.7% 15.2% .7% 13.9% 3504

Marsabit Central Ward None 14.5% 18.7% 31.3% 2.2% 18.9% .2% 1.3% 12.8% 1288

Marsabit Central Ward Primary 14.7% 13.7% 20.4% 1.8% 13.9% 20.0% .6% 15.0% 1254

Marsabit Central Ward Secondary+ 28.1% 7.3% 10.6% 4.2% 6.5% 29.1% .2% 14.0% 962

Laisamis Constituency Total 5.1% 10.9% 64.4% 1.0% 5.2% 6.8% .3% 6.4% 12091

Laisamis Constituency None 2.6% 11.3% 72.4% 1.0% 6.1% .3% .3% 6.1% 9755

Laisamis Constituency Primary 6.6% 10.6% 38.1% .9% 2.1% 34.6% .1% 6.9% 1499

Laisamis Constituency Secondary+ 31.3% 6.5% 18.6% 1.7% .6% 33.1% 0.0% 8.2% 837

Loiyangalani Ward Total 4.8% 10.0% 70.1% .4% 2.6% 5.4% .4% 6.1% 2702

Loiyangalani Ward None 3.2% 10.0% 77.2% .5% 2.8% .2% .5% 5.6% 2200

Loiyangalani Ward Primary 6.3% 11.1% 43.3% 0.0% 2.8% 30.5% 0.0% 6.0% 351

Loiyangalani Ward Secondary+ 25.2% 7.9% 28.5% .7% 0.0% 23.8% 0.0% 13.9% 151

Kargi/South Horr Ward Total 2.6% 18.8% 54.8% .6% 9.9% 8.0% .2% 5.0% 2017

Kargi/South Horr Ward None 1.6% 20.4% 60.9% .5% 11.4% .1% .2% 4.9% 1646

Kargi/South Horr Ward Primary 2.4% 14.6% 33.1% 1.2% 3.5% 39.8% .4% 5.1% 254

Kargi/South Horr Ward Secondary+ 17.9% 6.0% 16.2% 1.7% 3.4% 49.6% 0.0% 5.1% 117

Korr/Ngurunit Ward Total 3.2% 5.5% 77.8% 1.0% 2.9% 4.5% .1% 5.1% 3717

Korr/Ngurunit Ward None 1.6% 5.2% 83.8% .9% 3.3% .3% .1% 4.8% 3202

Korr/Ngurunit Ward Primary 7.7% 8.6% 46.7% 1.2% .9% 30.2% 0.0% 4.7% 338

Korr/Ngurunit Ward Secondary+ 23.7% 4.5% 28.8% 1.7% 0.0% 31.1% 0.0% 10.2% 177

Logologo Ward Total 5.5% 5.9% 51.4% 1.7% 5.9% 14.5% .4% 14.8% 1056

Logologo Ward None 4.2% 5.7% 61.3% 1.8% 7.6% 1.2% .4% 17.7% 733

Logologo Ward Primary 5.4% 5.4% 35.6% 1.4% 2.3% 42.3% .5% 7.2% 222

Logologo Ward Secondary+ 14.9% 7.9% 14.9% 2.0% 1.0% 49.5% 0.0% 9.9% 101

32



Laisamis Ward Total 9.8% 15.2% 52.2% 1.6% 7.2% 7.5% .4% 6.2% 2599

Laisamis Ward None 3.8% 16.9% 62.3% 1.6% 9.3% .1% .5% 5.5% 1974

Laisamis Ward Primary 9.9% 12.6% 29.3% 1.2% 1.5% 34.1% 0.0% 11.4% 334

Laisamis Ward Secondary+ 50.2% 6.5% 9.6% 2.1% 0.0% 26.8% 0.0% 4.8% 291

Table 25.6: Gini Coefficient by County, Constituency and Ward

County/Constituency/Wards Pop. Share Mean Consump. Share Gini

Kenya 1 3,440 1 0.445

Rural 0.688 2,270 0.454 0.361

Urban 0.312 6,010 0.546 0.368

Marsabit County 0.008 1,860 0.004 0.365

Moyale Constituency 0.003 2,050 0.0016 0.379

Butiye 0.000 1,940 0.0003 0.351

Sololo 0.000 2,070 0.0001 0.319

Heillu/Manyatta 0.001 2,460 0.0004 0.301

Golbo 0.001 1,270 0.0003 0.234

Moyale Township 0.000 4,310 0.0004 0.347

Uran 0.000 1,150 0.0001 0.225

Obbu 0.000 1,320 0.0001 0.223

North Horr Constituency 0.002 1,330 0.0008 0.214

Dukana 0.001 1,580 0.0002 0.163

Maikona 0.001 1,220 0.0002 0.223

Turbi 0.000 1,220 0.0001 0.222

North Horr 0.000 1,280 0.0002 0.222

Illeret 0.000 1,260 0.0001 0.174

Saku Constituency 0.001 2,900 0.0010 0.444

Sagante/Jaldesa 0.000 1,470 0.0002 0.253

Karare 0.000 1,740 0.0001 0.291

Marsabit Central 0.001 4,600 0.0007 0.387

Laisamis Constituency 0.002 1,430 0.0007 0.252

Loiyangalani 0.000 1,420 0.0002 0.244

Kargi/South Horr 0.000 1,380 0.0001 0.261

Korr/Ngurunit 0.001 1,330 0.0002 0.212

Logologo 0.000 1,560 0.0001 0.272

Laisamis 0.000 1,600 0.0002 0.290

Table 25.7: Education by County, Constituency and Wards

County/Constituency/Wards None Primary Secondary+ Total Pop

Kenya 25.2 52.0 22.8 34,024,396

Rural 29.5 54.7 15.9 23,314,262

Urban 15.8 46.2 38.0 10,710,134

Marsabit County 68.2 25.8 6.0 260,786

Moyale Constituency 58.5 34.2 7.2 93,671

Butiye 60.3 32.4 7.3 16,019

Sololo 48.5 39.8 11.8 7,557

33


Heillu/Manyatta 53.0 40.5 6.5 19,334

Golbo 70.8 26.3 2.9 25,926

Moyale Township 42.7 41.2 16.1 11,072

Uran 57.4 36.0 6.6 8,950

Obbu 63.4 29.4 7.3 4,813

North Horr Constituency 83.5 13.5 3.0 68,617

Dukana 90.1 8.1 1.8 17,234

Maikona 75.6 19.9 4.5 17,502

Turbi 76.7 19.7 3.6 9,358

North Horr 83.0 13.5 3.5 15,866

Illeret 94.8 4.1 1.1 8,657

Saku Constituency 47.2 41.3 11.5 41,433

Sagante/Jaldesa 53.8 39.5 6.7 15,117

Karare 63.0 32.0 5.1 7,916

Marsabit Central 35.0 46.7 18.3 18,400

Laisamis Constituency 81.0 15.6 3.4 57,065

Loiyangalani 82.5 14.6 3.0 14,316

Kargi/South Horr 78.6 17.8 3.7 8,485

Korr/Ngurunit 85.9 11.7 2.4 18,883

Logologo 70.2 24.8 5.0 4,401

Laisamis 76.8 18.3 4.9 10,980

Table 25.8: Education for Male and Female Headed Households by County, Constituency and Ward

County/Constituency/Wards None Primary Secondary+ Total Pop None Primary Secondary+ Total Pop

Kenya 23.5 51.8 24.7 16,819,031 26.8 52.2 21.0 17,205,365

Rural 27.7 54.9 17.4 11,472,394 31.2 54.4 14.4 11,841,868

Urban 14.4 45.2 40.4 5,346,637 17.2 47.2 35.6 5,363,497

Marsabit County 64.8 27.1 8.1 134,406 71.8 24.4 3.8 126,380

Moyale Constituency 54.0 36.3 9.8 48,742 63.5 32.0 4.5 44,929

Butiye 56.9 33.1 10.0 8,464 64.0 31.7 4.3 7,555

Sololo 43.6 40.9 15.4 3,803 53.4 38.6 8.0 3,754

Heillu/Manyatta 49.1 42.5 8.4 10,096 57.3 38.2 4.5 9,238

Golbo 64.9 30.8 4.3 13,626 77.4 21.3 1.3 12,300

Moyale Township 34.9 42.9 22.2 5,358 50.0 39.6 10.4 5,714

Uran 53.2 36.9 9.9 4,829 62.2 35.0 2.8 4,121

Obbu 61.9 28.9 9.2 2,566 65.0 30.0 5.0 2,247

North Horr Constituency 82.2 13.7 4.1 37,733 85.3 13.1 1.7 30,884

Dukana 89.0 8.4 2.7 9,826 91.7 7.8 0.5 7,408

34



Maikona 73.5 20.4 6.1 9,234 78.0 19.4 2.7 8,268

Turbi 75.5 19.8 4.7 5,066 78.2 19.6 2.2 4,292

North Horr 81.8 13.7 4.6 8,998 84.7 13.3 2.0 6,868

Illeret 93.1 5.3 1.7 4,609 96.9 2.7 0.4 4,048

Saku Constituency 42.1 42.8 15.1 20,505 52.2 39.8 8.1 20,928

Sagante/Jaldesa 47.7 42.6 9.8 7,553 60.0 36.5 3.5 7,564

Karare 60.3 33.0 6.7 3,871 65.5 31.0 3.6 4,045

Marsabit Central 29.8 47.2 23.0 9,081 40.0 46.3 13.7 9,319

Laisamis Constituency 77.3 17.5 5.2 27,426 84.4 13.9 1.7 29,639

Loiyangalani 78.9 16.8 4.2 7,089 86.0 12.3 1.7 7,227

Kargi/South Horr 75.0 19.1 5.9 4,020 81.8 16.6 1.7 4,465

Korr/Ngurunit 82.9 13.4 3.8 9,058 88.7 10.3 1.1 9,825

Logologo 68.3 24.6 7.2 2,174 72.0 25.0 3.0 2,227

Laisamis 70.7 21.5 7.8 5,085 82.0 15.6 2.4 5,895

Table 25.9: Cooking Fuel by County, Constituency and Wards

County/Constituency/Wards Electricity Paraffin LPG Biogas Firewood Charcoal Solar Other Households

Kenya 0.8 11.7 5.1 0.7 64.4 17.0 0.1 0.3 8,493,380

Rural 0.2 1.4 0.6 0.3 90.3 7.1 0.1 0.1 5,239,879

Urban 1.8 28.3 12.3 1.4 22.7 32.8 0.0 0.6 3,253,501

Marsabit County 0.2 1.4 0.2 0.1 91.5 6.2 0.0 0.2 53,599

Moyale Constituency 0.3 1.8 0.2 0.1 86.0 11.4 0.1 0.2 15,693

Butiye 0.1 0.6 0.0 0.1 94.5 4.5 0.1 0.1 2,758

Sololo 0.1 2.1 0.2 - 95.5 2.1 0.1 - 1,455

Heillu/Manyatta 0.8 0.8 - 0.0 92.4 5.5 0.1 0.4 2,933

Golbo 0.0 0.1 0.1 0.2 98.5 0.9 0.0 0.1 3,807

Moyale Township 0.8 8.7 0.9 0.5 25.0 63.7 0.1 0.2 2,212

Uran 0.1 0.6 - 0.1 98.2 1.1 0.1 - 1,631

Obbu 0.1 0.8 - - 98.3 0.6 - 0.2 897

North Horr Constituency 0.0 0.2 0.1 0.1 98.2 1.0 0.0 0.3 15,131

Dukana - - 0.0 0.0 99.5 0.3 0.1 0.1 3,724

Maikona - 0.4 0.2 0.2 97.9 1.0 0.0 0.3 3,836

Turbi - 0.6 0.2 0.2 98.1 0.8 0.0 0.2 2,494

North Horr 0.0 0.1 - 0.0 98.6 1.2 - 0.1 3,611

Illeret - 0.1 0.2 0.1 95.2 2.9 0.1 1.4 1,466

Saku Constituency 0.6 4.5 0.6 0.3 83.3 10.5 0.0 0.2 9,482

Sagante/Jaldesa 0.2 0.4 0.1 0.1 98.0 1.0 - 0.2 3,112

Karare 0.1 0.4 0.1 0.1 97.9 1.2 0.1 0.1 1,918

35


Marsabit Central 1.1 9.0 1.1 0.6 66.7 21.1 0.0 0.3 4,452

Laisamis Constituency 0.0 0.1 0.2 0.1 96.4 3.0 0.0 0.2 13,293

Loiyangalani 0.0 0.0 0.2 0.0 93.9 5.4 0.1 0.3 3,262

Kargi/South Horr - 0.1 0.1 - 97.1 2.7 - 0.1 1,838

Korr/Ngurunit - - 0.1 0.1 98.7 0.9 - 0.2 3,939

Logologo - - - 0.2 99.0 0.8 - - 1,095

Laisamis - 0.5 0.3 0.1 94.9 4.1 0.1 0.1 3,159

Table 25.10: Cooking Fuel for Male Headed Households by County, Constituency and Wards


Kenya 0.9 13.5 5.3 0.8 61.4 17.7 0.1 0.4 5,762,320

Rural 0.2 1.6 0.6 0.3 89.6 7.5 0.1 0.1 3,413,616

Urban 1.9 30.9 12.0 1.4 20.4 32.5 0.0 0.7 2,348,704

Marsabit County 0.2 2.0 0.3 0.2 90.7 6.4 0.0 0.2 33,330


Butiye 0.1 0.9 0.0 0.1 94.2 4.5 0.1 0.2 1,853

Sololo 0.0 3.1 0.4 0.0 93.6 2.8 0.1 0.0 828

Heillu/Manyatta 0.5 1.1 0.0 0.1 92.8 5.0 0.0 0.6 1,877

Golbo 0.0 0.1 0.2 0.2 98.3 1.0 0.0 0.1 2,672

Moyale Township 0.9 12.5 1.3 0.7 22.6 61.5 0.1 0.4 1,382

Uran 0.0 0.5 0.0 0.1 98.1 1.3 0.0 0.0 1,092

Obbu 0.2 1.1 0.0 0.0 97.7 0.6 0.0 0.3 621


Dukana 0.0 0.0 0.0 0.0 99.3 0.3 0.1 0.1 2,729

Maikona 0.0 0.5 0.2 0.2 97.5 1.2 0.0 0.3 2,656

Turbi 0.0 0.9 0.2 0.2 97.5 1.0 0.0 0.1 1,621

North Horr 0.0 0.1 0.0 0.0 98.2 1.6 0.0 0.1 2,505

Illeret 0.0 0.2 0.3 0.1 95.5 2.8 0.1 1.1 1,042


Sagante/Jaldesa 0.3 0.4 0.2 0.1 97.6 1.3 0.0 0.2 1,869

Karare 0.2 0.7 0.0 0.2 97.2 1.6 0.0 0.1 1,047



Loiyangalani 0.1 0.1 0.2 0.0 93.0 6.2 0.1 0.4 1,804

Kargi/South Horr 0.0 0.0 0.1 0.0 96.3 3.3 0.0 0.2 901

Korr/Ngurunit 0.0 0.0 0.1 0.0 98.5 1.1 0.0 0.2 2,004

Logologo 0.0 0.0 0.0 0.4 98.2 1.4 0.0 0.0 566

Laisamis 0.0 1.1 0.5 0.1 92.0 6.2 0.1 0.1 1,427

Table 25.11: Cooking Fuel for Female Headed Households by County, Constituency and Wards


Kenya 0.6 7.9 4.6 0.7 70.6 15.5 0.0 0.1 2,731,060

Rural 0.1 1.0 0.5 0.3 91.5 6.5 0.0 0.1 1,826,263

Urban 1.6 21.7 13.0 1.5 28.5 33.6 0.0 0.3 904,797

36



Marsabit County 0.2 0.5 0.1 0.1 93.0 6.0 0.1 0.1 20,269


Butiye 0.1 - 0.1 0.1 95.2 4.4 - - 905

Sololo 0.2 0.6 - - 97.9 1.3 - - 627

Heillu/Manyatta 1.2 0.3 - - 91.9 6.3 0.2 0.1 1,056

Golbo - - - 0.1 98.9 0.9 0.1 - 1,135

Moyale Township 0.6 2.4 0.2 0.2 28.9 67.5 0.1 - 830

Uran 0.2 0.6 - - 98.3 0.7 0.2 - 539

Obbu - - - - 99.6 0.4 - - 276

North Horr Constituency - 0.1 0.1 0.0 98.8 0.6 0.0 0.3 4,578

Dukana - - - - 99.8 0.2 - - 995

Maikona - 0.3 0.3 0.1 98.6 0.4 - 0.3 1,180

Turbi - - 0.1 - 99.2 0.3 0.1 0.2 873

North Horr - - - 0.1 99.4 0.5 - 0.1 1,106

Illeret - - - - 94.3 3.3 - 2.4 424


Sagante/Jaldesa - 0.3 0.1 0.2 98.5 0.6 - 0.3 1,243

Karare - 0.1 0.2 - 98.7 0.7 0.2 - 871


Laisamis Constituency - 0.0 0.1 0.0 97.5 2.2 0.0 0.1 6,591

Loiyangalani - - 0.1 0.1 95.0 4.5 0.1 0.3 1,458

Kargi/South Horr - 0.1 0.1 - 97.8 2.0 - - 937

Korr/Ngurunit - - 0.2 0.1 99.0 0.7 - 0.1 1,935

Logologo - - - - 99.8 0.2 - - 529

Laisamis - - 0.2 - 97.2 2.5 0.1 0.1 1,732

Table 25.12: Lighting Fuel by County, Constituency and Wards

County/Constituency/Wards ElectricityPressure Lamp Lantern Tin Lamp Gas Lamp Fuelwood Solar Other Households

Kenya 22.9 0.6 30.6 38.5 0.9 4.3 1.6 0.6 5,762,320

Rural 5.2 0.4 34.7 49.0 1.0 6.7 2.2 0.7 3,413,616

Urban 51.4 0.8 23.9 21.6 0.6 0.4 0.7 0.6 2,348,704

Marsabit County 7.7 0.1 23.7 12.1 2.9 47.1 1.2 5.2 33,330


Butiye 8.6 0.0 54.7 10.9 0.5 24.8 0.3 0.1 1,853

Sololo 1.2 0.1 49.3 23.2 7.6 13.9 4.1 0.6 828

Heillu/Manyatta 23.6 0.3 33.1 26.9 11.9 4.1 0.1 0.1 1,877

Golbo 0.5 0.1 54.3 20.3 3.2 20.9 0.2 0.5 2,672

Moyale Township 59.5 0.2 36.5 3.1 0.2 0.1 0.2 0.1 1,382

Uran 0.1 0.1 3.7 28.0 10.2 57.2 0.7 0.0 1,092

Obbu 0.1 0.0 23.9 16.2 5.9 51.3 0.4 2.2 621


Dukana 0.1 0.0 5.7 0.8 0.5 85.6 0.5 6.8 2,729

Maikona 0.2 0.1 13.3 7.8 0.9 51.9 1.2 24.6 2,656

Turbi 0.0 0.1 12.3 6.0 8.9 68.2 0.7 3.8 1,621

North Horr 0.1 0.1 5.0 5.5 0.6 61.9 2.5 24.3 2,505

Illeret 0.1 0.1 2.9 0.4 0.5 89.8 1.4 4.8 1,042

37



Sagante/Jaldesa 1.3 0.9 44.4 40.9 1.4 9.1 0.8 1.0 1,869

Karare 0.6 0.1 50.4 20.3 0.9 25.7 1.3 0.9 1,047



Loiyangalani 0.2 0.1 11.0 1.5 0.5 74.8 2.3 9.5 1,804

Kargi/South Horr 0.1 0.0 5.8 2.0 0.7 87.4 3.5 0.5 901

Korr/Ngurunit 0.1 0.0 5.8 1.7 0.6 89.7 0.8 1.3 2,004

Logologo 0.0 0.0 27.4 10.5 0.0 60.0 1.1 1.0 566

Laisamis 0.0 0.0 12.0 3.3 0.2 82.1 2.1 0.3 1,427

Table 25.13: Lighting Fuel for Male Headed Households by County, Constituency and Wards


Kenya 24.6 0.6 30.4 36.8 0.9 4.2 1.7 0.7 5,762,320

Rural 5.6 0.5 35.3 47.5 1.1 6.8 2.4 0.7 3,413,616

Urban 52.4 0.9 23.3 21.2 0.6 0.4 0.7 0.7 2,348,704

Marsabit County 8.2 0.1 23.0 11.8 2.9 47.4 1.3 5.4 33,330


Butiye 7.7 0.1 50.7 11.4 0.5 29.1 0.4 0.1 1,853

Sololo 1.6 0.2 48.9 23.3 8.0 12.0 5.4 0.6 828

Heillu/Manyatta 23.9 0.4 35.2 25.4 10.9 3.9 0.1 0.2 1,877

Golbo 0.4 0.1 49.8 21.5 3.6 23.8 0.1 0.6 2,672

Moyale Township 63.3 0.1 33.7 2.5 0.1 0.1 0.1 0.1 1,382

Uran 0.1 0.0 3.9 26.6 10.1 58.4 0.8 0.0 1,092

Obbu 0.2 0.0 18.5 14.5 6.9 58.0 0.5 1.4 621


Dukana 0.1 0.0 5.3 0.5 0.5 88.0 0.6 4.9 2,729

Maikona 0.2 0.1 11.4 7.6 0.9 54.1 1.4 24.3 2,656

Turbi 0.0 0.1 9.7 5.6 7.2 73.6 0.5 3.3 1,621

North Horr 0.1 0.1 4.5 4.8 0.7 63.7 2.2 24.0 2,505

Illeret 0.1 0.0 2.9 0.4 0.5 90.0 1.7 4.4 1,042


Sagante/Jaldesa 1.6 1.1 46.9 38.9 1.4 8.1 1.0 1.1 1,869

Karare 0.7 0.0 50.1 19.1 0.8 26.8 1.4 1.1 1,047



Loiyangalani 0.3 0.2 10.6 2.2 0.6 74.0 2.5 9.5 1,804

Kargi/South Horr 0.0 0.0 6.0 1.4 0.6 87.9 3.7 0.4 901

Korr/Ngurunit 0.1 0.0 6.2 1.8 0.4 88.8 1.1 1.5 2,004

Logologo 0.0 0.0 23.0 8.7 0.0 66.8 0.9 0.7 566

Laisamis 0.0 0.0 13.7 2.9 0.2 80.2 2.5 0.5 1,427

38



Table 25.14: Lighting Fuel for Female Headed Households by County, Constituency and Wards


Kenya 19.2 0.5 31.0 42.1 0.8 4.5 1.4 0.5 2,731,060

Rural 4.5 0.4 33.7 51.8 0.8 6.5 1.8 0.5 1,826,263

Urban 48.8 0.8 25.4 22.6 0.7 0.6 0.6 0.5 904,797

Marsabit County 6.8 0.1 24.9 12.5 2.9 46.7 1.2 4.9 20,269


Butiye 10.4 - 62.9 9.9 0.7 15.8 0.2 0.1 905

Sololo 0.8 - 49.9 23.0 7.0 16.4 2.2 0.6 627

Heillu/Manyatta 22.9 0.2 29.3 29.6 13.6 4.3 0.1 - 1,056

Golbo 0.7 0.1 64.8 17.4 2.3 13.9 0.4 0.4 1,135

Moyale Township 53.3 0.5 41.2 4.1 0.5 0.1 0.2 0.1 830

Uran 0.2 0.2 3.3 30.6 10.6 54.7 0.4 - 539

Obbu - - 35.9 19.9 3.6 36.2 0.4 4.0 276


Dukana - - 6.7 1.4 0.6 79.0 0.4 11.9 995

Maikona - 0.1 17.6 8.3 0.8 46.9 0.9 25.3 1,180

Turbi 0.1 0.1 17.0 6.8 12.1 58.1 1.0 4.8 873

North Horr - - 6.1 7.1 0.5 57.8 3.2 25.2 1,106

Illeret - 0.2 3.1 0.5 0.5 89.4 0.7 5.7 424


Sagante/Jaldesa 1.0 0.7 40.7 44.0 1.4 10.5 0.6 1.0 1,243

Karare 0.5 0.1 50.6 21.8 1.0 24.2 1.0 0.7 871



Loiyangalani 0.1 0.1 11.4 0.7 0.4 75.8 2.1 9.4 1,458

Kargi/South Horr 0.1 - 5.5 2.5 0.9 87.0 3.4 0.6 937

Korr/Ngurunit 0.1 - 5.3 1.6 0.8 90.6 0.5 1.1 1,935

Logologo - - 32.1 12.5 - 52.7 1.3 1.3 529

Laisamis - - 10.6 3.6 0.1 83.6 1.8 0.2 1,732

Table 25.15: Main material of the Floor by County, Constituency and Wards

County/Constituency/ wards Cement Tiles Wood Earth Other Households

Kenya 41.2 1.6 0.7 56.0 0.5 8,493,380

Rural 22.1 0.3 0.7 76.5 0.4 5,239,879

Urban 71.8 3.5 0.9 23.0 0.8 3,253,501

Marsabit County 16.6 0.3 0.3 82.0 0.9 53,599

Moyale Constituency 31.7 0.6 0.3 66.0 1.4 15,693

Butiye 32.0 0.1 0.3 65.8 1.7 2,758

Sololo 29.1 0.1 0.1 69.3 1.3 1,455

Heillu/Manyatta 47.7 0.4 0.5 48.3 2.9 2,933

Golbo 4.9 - 0.2 94.2 0.7 3,807

Moyale Township 84.9 3.4 0.1 10.4 1.2 2,212

Uran 7.1 - 0.1 92.4 0.5 1,631

Obbu 10.3 - 0.3 88.5 0.9 897

North Horr Constituency 1.7 0.0 0.2 97.6 0.5 15,131

39


Dukana 0.8 0.0 0.2 98.8 0.2 3,724

Maikona 1.9 0.0 0.1 97.7 0.2 3,836

Turbi 2.7 0.0 0.0 96.2 1.0 2,494

North Horr 1.3 0.1 0.2 98.2 0.2 3,611

Illeret 2.2 - 0.8 95.0 2.1 1,466

Saku Constituency 30.1 0.4 0.5 68.5 0.5 9,482

Sagante/Jaldesa 4.1 0.3 0.6 94.5 0.5 3,112

Karare 10.0 0.1 0.3 89.3 0.4 1,918

Marsabit Central 57.0 0.6 0.4 41.4 0.6 4,452

Laisamis Constituency 6.3 0.0 0.2 92.6 0.9 13,293

Loiyangalani 4.1 0.0 0.2 95.3 0.4 3,262

Kargi/South Horr 7.9 0.2 0.4 91.2 0.3 1,838

Korr/Ngurunit 3.0 - 0.2 95.6 1.1 3,939

Logologo 13.7 - 0.1 81.8 4.4 1,095

Laisamis 9.0 0.1 0.1 90.7 0.1 3,159

Table 25.16: Main Material of the Floor in Male and Female Headed Households by County, Constituency and Ward

County/Constituency/ wards Cement Tiles Wood Earth Other Households Cement Tiles Wood Earth Other Households

Kenya 42.8

1.6

0.8

54.2

0.6 5,762,320

37.7

1.4

0.7

59.8

0.5 2,731,060

Rural 22.1

0.3

0.7

76.4

0.4 3,413,616

22.2

0.3

0.6

76.6

0.3 1,826,263

Urban 72.9

3.5

0.9

21.9

0.8 2,348,704

69.0

3.6

0.9

25.8

0.8 904,797

Marsabit County 16.9

0.4

0.3

81.6

0.9 33,330

16.3

0.1

0.3

82.5

0.9 20,269

Moyale Constituency 30.1

0.8

0.3

67.6

1.2 10,325

34.8

0.2

0.2

63.0

1.8 5,368

Butiye 29.5

0.1

0.4

68.5

1.5 1,853

37.1

0.2

0.2

60.3

2.1 905

Sololo 31.4

0.2

0.2

66.9

1.2 828

26.0

-

-

72.6

1.4 627

Heillu/Manyatta 47.7

0.4

0.6

49.2

2.1 1,877

47.7

0.5

0.5

46.9

4.5 1,056

Golbo 4.2

-

0.2

94.9

0.7 2,672

6.4

-

0.4

92.4

0.8 1,135

Moyale Township 83.2

5.1

0.1

10.5

1.1 1,382

87.7

0.6

0.1

10.2

1.3 830

Uran 8.2

-

0.1

91.1

0.5 1,092

4.6

-

-

95.0

0.4 539

Obbu 8.9

-

0.5

89.4

1.3 621

13.4

-

-

86.6

- 276

North Horr Constituency 1.8

0.0

0.2

97.5

0.5 10,553

1.4

0.0

0.3

97.7

0.7 4,578

Dukana 0.8

0.0

0.2

98.9

0.1 2,729

0.7

-

0.1

98.6

0.6 995

Maikona 2.3

0.0

0.1

97.3

0.2 2,656

1.0

-

0.1

98.6

0.3 1,180

Turbi 2.0

-

-

96.9

1.1 1,621

4.0

0.1

0.1

95.0

0.8 873

North Horr 1.7

0.1

0.2

97.9

0.2 2,505

0.5

-

0.5

98.7

0.4 1,106

Illeret 2.6

-

0.7

94.7

2.0 1,042

1.2

-

0.9

95.5

2.4 424

40



Saku Constituency 32.6

0.5

0.4

65.9

0.5 5,750

26.2

0.3

0.5

72.4

0.6 3,732

Sagante/Jaldesa 4.2

0.3

0.6

94.4

0.5 1,869

3.9

0.3

0.6

94.7

0.6 1,243

Kararet 10.2

0.1

0.3

88.9

0.5 1,047

9.6

-

0.3

89.7

0.3 871

Marsabit Central 59.7

0.7

0.4

38.7

0.6 2,834

52.3

0.4

0.6

46.0

0.7 1,618

Laisamis Constituency 6.7

0.1

0.2

91.7

1.3 6,702

5.8

0.0

0.2

93.6

0.4 6,591

Loiyangalani 4.7

-

0.3

94.5

0.5 1,804

3.4

0.1

0.1

96.3

0.2 1,458

Kargi/South Horr 8.2

0.3

0.2

90.7

0.6 901

7.7

-

0.5

91.8

- 937

Korr/Ngurunit 3.5

-

0.2

95.0

1.2 2,004

2.5

-

0.1

96.3

1.0 1,935

Logologo 11.7

-

-

80.2

8.1 566

15.9

-

0.2

83.6

0.4 529

Laisamis 10.8

0.1

0.1

88.9

0.1 1,427

7.6

-

0.1

92.3

0.1 1,732

Table 25.17: Main Roofing Material by County Constituency and Wards

County/Constituency/WardsCorrugated Iron Sheets Tiles

Con-crete

Asbestos sheets Grass Makuti Tin

Mud/Dung Other Households

Kenya 73.5 2.2 3.6 2.2 13.3 3.2 0.3 0.8 1.0 8,493,380

Rural 70.3 0.7 0.2 1.8 20.2 4.2 0.2 1.2 1.1 5,239,879

Urban 78.5 4.6 9.1 2.9 2.1 1.5 0.3 0.1 0.9 3,253,501

Marsabit County 28.0 0.2 0.1 1.1 24.0 10.9 1.5 0.8 33.5 53,599

Moyale Constituency 44.8 0.2 0.1 1.3 47.7 0.9 0.0 1.2 3.8 15,693

Butiye 48.7 0.2 0.3 0.0 39.4 3.9 0.0 5.5 1.9 2,758

Sololo 52.9 0.2 0.1 0.0 44.9 0.3 0.0 0.1 1.5 1,455

Heillu/Manyatta 65.8 0.3 0.0 0.0 25.0 0.1 0.0 0.4 8.3 2,933

Golbo 11.7 0.2 0.0 1.5 80.9 0.1 0.1 0.6 4.9 3,807

Moyale Township 90.8 0.3 0.0 6.6 0.9 0.1 0.0 0.0 1.2 2,212

Uran 20.5 0.2 0.0 0.0 78.5 0.2 0.1 0.1 0.4 1,631

Obbu 22.5 0.1 0.0 0.0 69.9 1.3 0.0 0.1 6.0 897

North Horr Constituency 7.2 0.1 0.0 0.3 11.4 4.6 3.4 0.1 72.9 15,131

Dukana 4.5 0.1 0.1 0.0 16.0 3.9 0.3 0.1 75.0 3,724

Maikona 8.5 0.1 0.1 0.8 10.7 6.8 0.4 0.2 72.3 3,836

Turbi 5.9 0.1 0.0 0.0 5.1 0.1 0.8 0.0 88.0 2,494

North Horr 7.6 0.1 0.0 0.1 0.4 7.5 0.1 0.0 84.2 3,611

Illeret 11.3 0.1 0.1 0.3 39.2 1.6 31.9 0.1 15.5 1,466

Saku Constituency 61.5 0.6 0.1 3.1 25.7 1.3 1.4 1.0 5.4 9,482

Sagante/Jaldesa 35.9 0.8 0.1 3.9 56.9 0.3 0.0 0.0 2.2 3,112

Karare 34.1 0.3 0.1 0.3 30.7 6.1 3.0 4.4 21.1 1,918

Marsabit Central 91.2 0.6 0.1 3.7 1.7 0.0 1.8 0.1 0.8 4,452

Laisamis Constituency 7.8 0.1 0.0 0.3 9.3 36.5 1.1 1.1 43.8 13,293

Loiyangalani 6.6 0.1 0.0 0.9 15.8 49.8 0.1 1.0 25.7 3,262

Kargi/South Horr 9.0 0.3 0.1 0.0 1.7 65.2 5.3 3.6 14.9 1,838

Korr/Ngurunit 3.7 0.0 0.0 0.1 4.8 33.5 0.1 0.3 57.6 3,939

Logologo 15.7 0.0 0.1 0.1 5.8 12.1 0.2 2.8 63.1 1,095

Laisamis 11.0 0.1 0.0 0.0 13.9 18.4 1.1 0.2 55.4 3,159

41


Table 25.18: Main Roofing Material in Male Headed Households by County, Constituency and Wards

County/Constit-uency/Wards

Corrugated Iron Sheets Tiles Concrete

Asbestos sheets Grass Makuti Tin Mud/ Dung Other Households

Kenya 73.0 2.3 3.9 2.3 13.5 3.2 0.3 0.5 1.0 5,762,320

Rural 69.2 0.8 0.2 1.8 21.5 4.4 0.2 0.9 1.1 3,413,616

Urban 78.5 4.6 9.3 2.9 2.0 1.4 0.3 0.1 0.9 2,348,704

Marsabit County 27.8 0.2 0.1 1.1 25.8 8.3 1.5 0.8 34.4 33,330 Moyale Constit-uency 42.0 0.2 0.1 1.5 50.1 1.1 0.1 1.3 3.6 10,325

Butiye 43.6 0.1 0.4 - 43.4 5.2 - 5.7 1.6 1,853

Sololo 55.0 0.2 - - 43.0 0.2 - 0.1 1.4 828

Heillu/Manyatta 65.5 0.3 - - 26.0 0.1 - 0.5 7.6 1,877

Golbo 10.0 0.2 - 1.5 82.9 0.1 0.1 0.6 4.6 2,672

Moyale Township 89.4 0.4 - 8.1 0.7 0.1 0.1 0.1 1.1 1,382

Uran 20.8 0.2 - - 77.9 0.3 0.2 0.2 0.5 1,092

Obbu 19.0 0.2 - - 71.7 1.6 - 0.2 7.4 621 North Horr Constituency 6.9 0.1 0.1 0.3 12.8 3.6 3.3 0.1 72.9 10,553

Dukana 4.0 0.1 0.1 - 20.4 3.3 0.4 0.1 71.4 2,729

Maikona 8.8 0.1 0.1 1.0 9.6 5.2 0.6 0.3 74.3 2,656

Turbi 5.1 0.1 - 0.1 4.0 0.1 1.0 - 89.7 1,621

North Horr 6.9 0.0 - 0.1 0.4 5.4 0.2 - 87.1 2,505

Illeret 12.2 0.1 0.2 0.1 44.4 1.6 28.4 - 13.0 1,042 Saku Constit-uency 63.3 0.7 0.1 2.9 24.3 1.1 1.8 1.0 4.9 5,750

Sagante/Jaldesa 38.1 1.0 0.1 3.6 55.2 0.2 - - 1.8 1,869

Karare 34.0 0.3 - 0.3 30.5 5.7 2.7 5.0 21.6 1,047

Marsabit Central 90.7 0.6 0.1 3.4 1.7 - 2.7 0.1 0.8 2,834 Laisamis Con-stituency 8.5 0.1 0.0 0.2 10.1 32.9 0.8 1.0 46.4 6,702

Loiyangalani 7.2 0.1 - 0.6 17.9 44.0 0.1 1.0 29.2 1,804

Kargi/South Horr 9.8 0.2 0.1 - 2.8 57.5 4.0 3.1 22.5 901

Korr/Ngurunit 4.1 - - - 6.2 29.7 - 0.3 59.6 2,004

Logologo 14.1 - - 0.2 9.7 13.4 - 2.5 60.1 566

Laisamis 13.5 - - 0.1 10.5 15.3 1.0 0.1 59.4 1,427

Table 25.19: Main Roofing Material in Female Headed Households by County, Constituency and Wards

County/Constit-uency/Wards

Corrugated Iron Sheets Tiles Concrete

Asbestos sheets Grass Makuti Tin Mud/Dung Other Households

Kenya 74.5 2.0 3.0 2.2 12.7 3.2 0.3 1.2 1.0 2,731,060

Rural 72.5 0.7 0.1 1.8 17.8 3.9 0.3 1.8 1.1 1,826,263

Urban 78.6 4.5 8.7 2.9 2.3 1.6 0.3 0.1 0.9 904,797

Marsabit County 28.2 0.2 0.0 1.0 21.1 15.1 1.5 0.9 32.0 20,269 Moyale Constit-uency 50.2 0.2 0.1 1.0 43.0 0.3 0.0 1.1 4.1 5,368

Butiye 59.3 0.3 0.1 0.1 31.0 1.1 0.1 5.3 2.5 905

Sololo 50.1 0.2 0.3 - 47.4 0.5 - - 1.6 627

Heillu/Manyatta 66.5 0.2 - 0.1 23.3 0.2 - 0.2 9.6 1,056

Golbo 15.6 0.3 - 1.7 76.0 0.1 - 0.7 5.6 1,135

42



Moyale Township 93.0 0.1 - 4.2 1.3 - - - 1.3 830

Uran 19.9 0.2 - - 79.8 - - - 0.2 539

Obbu 30.4 - - - 65.9 0.7 - - 2.9 276 North Horr Constituency 7.8 0.1 - 0.2 8.1 7.0 3.9 0.0 72.9 4,578

Dukana 5.6 0.2 - - 3.8 5.5 0.1 - 84.7 995

Maikona 7.8 0.1 - 0.3 13.4 10.4 - 0.1 67.9 1,180

Turbi 7.3 0.1 - - 7.2 0.1 0.3 - 84.9 873

North Horr 9.4 0.1 - - 0.3 12.4 0.1 - 77.8 1,106

Illeret 9.2 - - 0.7 26.2 1.4 40.6 0.2 21.7 424 Saku Constit-uency 58.7 0.5 0.1 3.4 27.7 1.7 0.9 1.0 6.1 3,732

Sagante/Jaldesa 32.5 0.5 0.1 4.4 59.4 0.4 - - 2.7 1,243

Karare 34.2 0.3 0.1 0.2 30.9 6.5 3.4 3.8 20.4 871

Marsabit Central 92.0 0.6 0.1 4.4 1.7 0.1 0.1 0.2 0.9 1,618 Laisamis Con-stituency 7.1 0.1 0.0 0.3 8.6 40.3 1.4 1.2 41.1 6,591

Loiyangalani 5.9 0.1 - 1.2 13.3 57.1 0.1 1.0 21.3 1,458

Kargi/South Horr 8.2 0.4 - - 0.6 72.6 6.6 4.1 7.5 937

Korr/Ngurunit 3.2 0.1 0.1 0.2 3.4 37.4 0.2 0.2 55.5 1,935

Logologo 17.4 - 0.2 - 1.7 10.8 0.4 3.2 66.4 529

Laisamis 8.9 0.1 - - 16.7 20.8 1.2 0.2 52.1 1,732

Table 25.20: Main material of the wall by County, Constituency and Wards

County/Constituency/Wards Stone Brick/Block

Mud/Wood

Mud/Ce-ment

Wood only

Corrugated Iron Sheets

Grass/Reeds Tin Other Households

Kenya 16.7 16.9 36.5 7.7 11.1 6.7 3.0 0.3 1.2 8,493,380

Rural 5.7 13.8 50.0 7.6 14.4 2.5 4.4 0.3 1.4 5,239,879

Urban 34.5 21.9 14.8 7.8 5.8 13.3 0.8 0.3 0.9 3,253,501

Marsabit County 1.5 5.0 30.6 11.0 7.5 1.1 11.3 3.4 28.6 53,599


Butiye 0.5 8.4 45.6 20.7 0.8 0.1 22.2 0.0 1.7 2,758

Sololo 0.3 5.8 69.5 20.3 0.6 1.1 0.8 0.0 1.5 1,455

Heillu/Manyatta 0.4 8.0 44.5 28.4 3.1 0.1 8.4 0.0 7.1 2,933

Golbo 0.2 1.1 50.7 6.0 3.7 0.1 34.8 0.1 3.4 3,807

Moyale Township 5.2 43.9 17.2 30.8 0.4 0.0 1.1 0.1 1.2 2,212

Uran 0.2 0.7 63.6 10.8 1.0 0.4 23.0 0.0 0.2 1,631 Obbu 0.0 0.3 49.4 6.8 3.8 0.1 35.9 0.1 3.6 897

North Horr Constituency 1.7 0.7 9.1 2.2 3.4 0.9 7.6 2.8 71.6 15,131

Dukana 0.2 0.2 4.2 0.8 6.3 0.2 7.6 0.4 80.1 3,724

Maikona 1.0 1.0 15.8 4.2 1.1 0.3 4.4 0.4 71.8 3,836

Turbi 6.5 0.4 5.3 1.7 0.1 0.2 2.8 0.7 82.3 2,494

North Horr 0.9 0.6 12.2 2.2 4.3 0.0 0.2 0.2 79.4 3,611

Illeret 0.6 1.8 3.1 1.2 5.9 7.9 42.4 25.2 11.9 1,466


Sagante/Jaldesa 0.4 1.6 82.8 6.2 2.8 2.1 3.3 0.0 0.9 3,112

Karare 1.8 2.0 79.0 10.7 1.7 1.6 0.6 1.1 1.4 1,918

Marsabit Central 5.4 12.9 35.8 40.9 1.5 2.5 0.2 0.1 0.7 4,452

43


Laisamis Constituency 1.0 2.6 14.9 3.8 22.3 1.5 14.1 10.1 29.7 13,293

Loiyangalani 1.7 1.1 32.0 7.0 14.2 0.4 23.8 0.0 19.9 3,262

Kargi/South Horr 2.4 1.1 17.5 5.4 28.1 0.4 15.0 1.3 28.9 1,838

Korr/Ngurunit 0.3 2.3 6.3 1.0 27.1 0.2 16.5 10.8 35.4 3,939

Logologo 0.3 1.2 26.6 8.0 33.1 1.9 0.3 18.2 10.5 1,095

Laisamis 0.5 6.0 2.2 1.5 17.6 4.7 5.5 22.0 39.9 3,159

Table 25.21: Main Material of the Wall in Male Headed Households by County, Constituency and Ward

County/ Constituen-cy/ Wards Stone

Brick/Block Mud/Wood

Mud/Ce-ment Wood only

Corrugat-ed Iron Sheets

Grass/Reeds Tin Other

House-holds

Kenya 17.5 16.6 34.7 7.6 11.4 7.4 3.4 0.3 1.2 5,762,320

Rural 5.8 13.1 48.9 7.3 15.4 2.6 5.2 0.3 1.4 3,413,616

Urban 34.6 21.6 14.0 7.9 5.6 14.4 0.7 0.3 0.9 2,348,704

Marsabit County 1.8 5.4 28.7 10.8 6.6 1.1 12.6 2.7 30.3 33,330


Butiye 0.5 8.0 43.7 19.2 0.9 0.1 26.2 - 1.5 1,853

Sololo 0.2 6.6 67.5 20.9 0.4 1.7 1.2 - 1.4 828

Heillu/Manyatta 0.4 8.0 44.3 28.4 3.4 0.1 9.1 - 6.2 1,877

Golbo 0.1 1.2 48.2 5.1 3.9 0.2 38.1 0.1 3.3 2,672

Moyale Township 5.6 47.0 16.1 28.9 0.3 - 0.9 0.1 1.2 1,382

Uran 0.1 0.7 59.1 12.0 1.3 0.5 25.9 - 0.4 1,092

Obbu - 0.3 41.2 7.1 3.9 - 42.5 0.2 4.8 621 North Horr Constit-uency 1.9 0.8 7.4 1.8 3.7 1.1 8.3 2.7 72.3 10,553

Dukana 0.1 0.3 3.6 0.8 6.4 0.2 7.7 0.5 80.4 2,729

Maikona 1.4 1.3 13.5 3.8 1.1 0.3 4.7 0.6 73.5 2,656

Turbi 8.0 0.3 3.6 1.4 0.1 0.3 2.8 1.0 82.5 1,621

North Horr 1.2 0.6 9.7 1.5 5.0 - 0.2 0.2 81.7 2,505

Illeret 0.7 2.2 2.5 1.1 5.9 9.1 47.3 22.0 9.3 1,042


Sagante/Jaldesa 0.4 1.8 82.3 6.4 2.8 2.2 3.3 - 0.8 1,869

Karare 1.6 1.7 77.4 11.1 2.7 2.1 0.7 0.8 2.0 1,047

Marsabit Central 6.2 14.4 34.1 40.1 1.6 2.7 0.2 0.1 0.7 2,834 Laisamis Constit-uency 1.4 3.0 12.9 3.8 21.7 1.3 15.1 9.0 31.9 6,702

Loiyangalani 2.3 1.3 27.0 6.4 15.1 0.4 23.6 - 23.9 1,804

Kargi/South Horr 3.3 1.3 13.5 5.2 28.9 0.3 24.2 1.2 22.0 901

Korr/Ngurunit 0.5 2.6 4.9 1.1 26.0 0.2 15.4 11.5 37.8 2,004

Logologo - 0.5 22.1 7.4 33.6 2.8 0.4 14.8 18.4 566

Laisamis 0.7 7.7 2.2 1.8 14.8 4.1 4.0 19.3 45.3 1,427

44



Table 25.22: Main Material of the Wall in Female Headed Households by County, Constituency and Ward

County/ Constitu-ency Stone

Brick/Block Mud/Wood

Mud/Ce-ment Wood only

Corrugated Iron Sheets

Grass/Reeds Tin Other

House-holds

Kenya 15.0 17.5 40.4 7.9 10.5 5.1 2.1 0.3 1.2 2,731,060

Rural 5.4 14.9 52.1 8.0 12.6 2.4 2.8 0.4 1.4 1,826,263

Urban 34.2 22.6 16.9 7.6 6.2 10.5 0.8 0.3 0.9 904,797

Marsabit County 1.1 4.5 33.7 11.4 8.9 1.0 9.2 4.5 25.8 20,269


Butiye 0.3 9.3 49.5 23.9 0.8 0.1 13.9 - 2.2 905

Sololo 0.5 4.6 72.1 19.6 1.0 0.3 0.3 - 1.6 627

Heillu/Manyatta 0.6 8.0 44.8 28.2 2.6 0.2 7.0 - 8.6 1,056

Golbo 0.4 0.9 56.7 8.0 3.3 - 26.9 0.1 3.7 1,135

Moyale Township 4.7 38.8 19.2 33.9 0.6 0.1 1.4 - 1.3 830

Uran 0.4 0.7 72.7 8.3 0.6 0.2 17.1 - - 539

Obbu - 0.4 67.8 6.2 3.6 0.4 21.0 - 0.7 276 North Horr Constit-uency 1.0 0.4 13.1 2.9 2.8 0.5 6.0 3.2 70.0 4,578

Dukana 0.5 - 5.9 0.9 5.9 - 7.4 0.1 79.2 995

Maikona 0.3 0.4 21.1 5.0 1.1 0.2 3.7 0.1 68.1 1,180

Turbi 3.7 0.6 8.4 2.3 0.1 0.1 3.0 - 81.9 873

North Horr 0.3 0.5 18.0 3.7 2.8 0.1 0.1 0.3 74.2 1,106

Illeret 0.5 0.7 4.7 1.4 5.9 5.0 30.4 33.0 18.4 424


Sagante/Jaldesa 0.5 1.3 83.5 5.9 2.7 1.9 3.3 - 1.0 1,243

Karare 2.1 2.4 81.1 10.3 0.6 0.9 0.6 1.5 0.6 871

Marsabit Central 4.0 10.4 38.8 42.4 1.5 2.1 0.1 0.1 0.7 1,618 Laisamis Constit-uency 0.6 2.3 16.9 3.8 22.9 1.6 13.2 11.3 27.5 6,591

Loiyangalani 1.0 0.8 38.3 7.7 13.1 0.3 23.9 - 14.9 1,458

Kargi/South Horr 1.5 0.9 21.2 5.5 27.3 0.4 6.2 1.4 35.5 937

Korr/Ngurunit 0.2 2.0 7.8 1.0 28.2 0.2 17.6 10.1 33.0 1,935

Logologo 0.6 1.9 31.4 8.7 32.5 0.9 0.2 21.7 2.1 529

Laisamis 0.4 4.7 2.3 1.2 19.9 5.1 6.8 24.3 35.4 1,732

45


Tabl

e 25.2

3: S

ourc

e of W

ater

by C

ount

y, Co

nstit

uenc

y and

War

d

Coun

ty/C

onst

ituen

cy/

War

dsPo

ndDa

mLa

keSt

ream

/Ri

ver

Unpr

otec

ted

Sprin

gUn

prot

ecte

d W

ellJa

bia

Wat

er

vend

orOt

her

Unim

prov

ed

Sour

ces

Prot

ecte

d Sp

ring

Prot

ecte

d W

ellBo

reho

lePi

ped

into

Dw

ellin

gPi

ped

Rain

W

ater

Co

llect

ion

Impr

oved

So

urce

sNu

mbe

r of

Indi

vidua

ls

Keny

a2.7

2.41.2

23.2

5.06.9

0.35.2

0.447

.47.6

7.711

.65.9

19.2

0.752

.6

37,9

19,64

7

Rura

l3.6

3.21.5

29.6

6.48.7

0.42.2

0.556

.09.2

8.112

.01.8

12.1

0.844

.0

26,0

75,19

5

Urba

n0.9

0.70.5

9.21.9

2.90.2

11.8

0.128

.34.0

6.810

.714

.734

.90.5

71.7

1

1,844

,452

Marsa

bit C

ounty

7.54.3

1.40.6

1.235

.71.3

10.1

0.162

.21.7

10.1

18.8

0.26.3

0.637

.8

288,4

13

Moya

le C

onsti

tuenc

y20

.911

.00.2

0.11.0

10.4

0.510

.70.2

55.0

1.212

.626

.00.1

4.50.6

45.0

10

2,905

Butiy

e2.2

9.90.0

0.10.1

9.40.0

0.40.2

22.2

4.138

.034

.00.0

1.50.1

77.8

1

7,447

Solol

o55

.90.1

0.00.1

0.31.7

0.10.0

0.058

.20.0

0.541

.10.0

0.00.2

41.8

8,219

Heillu

/Man

yatta

7.920

.61.0

0.43.3

36.7

0.00.7

0.070

.61.6

20.4

6.30.1

0.00.9

29.4

2

1,116

Golbo

33.2

15.2

0.10.0

0.02.4

0.116

.00.0

67.0

0.00.0

19.7

0.013

.30.0

33.0

2

8,905

Moya

le To

wnsh

ip0.1

6.70.0

0.00.0

3.40.7

50.7

1.162

.71.2

13.8

14.4

0.74.2

2.937

.3

12,3

08

Uran

11.3

0.00.0

0.02.4

0.30.0

0.00.0

14.0

0.33.1

82.6

0.00.0

0.086

.0

9,7

21

Obbu

79.7

0.00.0

0.20.0

0.07.1

0.00.2

87.1

0.00.1

12.8

0.00.0

0.012

.9

5,1

89

North

Hor

r Co

nstitu

ency

0.00.5

1.60.2

1.762

.44.1

2.10.0

72.8

1.412

.712

.90.0

0.10.1

27.2

7

4,669

Duka

na0.0

0.10.0

0.00.9

98.1

0.00.0

0.099

.00.0

0.80.1

0.00.0

0.01.0

1

8,598

Maiko

na0.0

1.70.0

0.03.2

51.5

16.0

1.90.1

74.4

3.421

.40.5

0.10.0

0.225

.6

19,1

32

Turb

i0.1

0.00.0

0.00.0

0.20.0

12.0

0.012

.30.0

0.187

.50.0

0.00.0

87.7

1

0,295

North

Hor

r0.1

0.00.2

0.10.8

64.0

0.00.0

0.065

.12.4

30.0

2.40.0

0.10.0

34.9

1

7,157

Illere

t0.1

0.112

.21.7

4.179

.20.0

0.00.0

97.4

0.10.8

0.90.1

0.70.0

2.6

9,4

87

Saku

Con

stitue

ncy

0.10.3

0.10.1

0.928

.80.4

35.9

0.166

.64.8

5.711

.80.4

9.21.4

33.4

4

5,893

Saga

nte/Ja

ldesa

0.00.0

0.00.0

1.847

.00.2

12.0

0.261

.12.4

7.421

.00.1

7.60.3

38.9

1

6,740

Kara

re0.0

0.00.0

0.01.5

49.2

0.00.0

0.150

.719

.64.4

9.60.0

15.6

0.049

.3

8,9

54

Marsa

bit C

entra

l0.1

0.80.1

0.10.0

4.80.7

71.6

0.178

.30.3

4.85.1

0.97.8

2.821

.7

20,1

99

Laisa

mis

Cons

tituen

cy0.2

0.83.9

2.31.1

49.8

0.10.0

0.058

.20.8

6.419

.20.4

14.4

0.541

.8

64,9

46

Loiya

ngala

ni0.7

2.915

.63.2

3.420

.70.0

0.00.0

46.5

0.16.5

0.81.2

44.8

0.053

.5

16,1

91

Karg

i/Sou

th Ho

rr0.0

0.00.0

7.70.2

69.8

0.00.0

0.077

.80.1

1.95.8

0.014

.40.0

22.2

9,753

Korr/

Ngur

unit

0.00.3

0.01.0

0.372

.40.0

0.00.0

74.2

2.16.4

15.7

0.00.0

1.725

.8

21,2

56

Logo

logo

0.00.0

0.00.0

0.00.1

0.00.0

0.00.1

0.00.0

99.9

0.00.0

0.099

.9

5,1

19

Laisa

mis

0.00.0

0.00.0

0.753

.70.3

0.00.0

54.7

0.412

.426

.40.6

5.40.0

45.3

1

2,627

46



Tabl

e 25.2

4: S

ourc

e of W

ater

of M

ale h

eade

d Ho

useh

old

by C

ount

y Con

stitu

ency

and

War

d

Coun

ty/C

onst

ituen

cy/

War

dsPo

ndDa

mLa

keSt

ream

/Ri

ver

Unpr

o-te

cted

Sp

ring

Unpr

o-te

cted

W

ellJa

bia

Wat

er

vend

orOt

her

Unim

-pr

oved

So

urce

s

Pro-

tect

ed

Sprin

gPr

otec

ted

Well

Bore

-ho

le

Pipe

d in

to

Dwell

ing

Pipe

d

Rain

W

ater

Co

llec-

tion

Impr

oved

So

urce

sNu

mbe

r of

Indi

vidua

ls

Keny

a

2.7

2.3

1.1

22.4

4.8

6.7

0.4

5.6

0.4

46

.4

7.4

7.7

11.7

6.2

19

.9

0.7

53.6

26

,755,0

66

Rura

l

3.7

3.1

1.4

29.1

6.3

8.6

0.4

2.4

0.5

55

.6

9.2

8.2

12.1

1.9

12

.2

0.8

44.4

18

,016,4

71

Urba

n

0.8

0.6

0.5

8.5

1.8

2.8

0.2

12.1

0.1

27

.5

3.8

6.7

10.8

14

.9

35.8

0.5

72

.5

8,738

,595

Marsa

bit C

ounty

7.8

4.9

1.6

0.6

1.3

36

.1

1.4

10.1

0.1

63

.8

1.6

9.5

18.7

0.2

5.6

0.6

36

.2

193,3

22

Moya

le C

onsti

tuenc

y

20.8

12

.3

0.2

0.1

0.8

9.6

0.5

11.2

0.2

55

.8

1.1

11.3

26

.6

0.1

4.5

0.6

44.2

71,60

4

Butiy

e

2.5

11.0

0.1

0.0

0.1

8.9

-

0.3

0.2

23

.1

3.8

32.0

39

.5

-

1.4

0.1

76.9

12,32

1

Solol

o

55.2

0.1

-

0.1

0.2

1.5

0.2

0.0

-

57

.4

-

0.7

41.7

-

-

0.2

42

.6

5,0

65

Heillu

/Man

yatta

8.5

22

.0

0.9

0.4

2.9

35.4

-

0.6

-

70

.8

1.5

20.4

6.4

0.1

-

0.9

29

.2

14

,070

Golbo

31

.5

17.6

0.1

0.0

-

2.3

0.1

18

.6

-

70.2

0.0

0.0

17

.5

-

12.3

-

29

.8

21

,959

Moya

le To

wnsh

ip

0.1

7.1

-

-

0.0

3.0

0.5

50.1

1.1

62

.0

1.4

13.0

14

.2

0.9

4.5

3.9

38.0

7,523

Uran

11

.2

-

-

-

2.5

0.4

-

-

-

14.2

0.4

3.5

81

.9

-

-

-

85.8

6,823

Obbu

75

.2

-

-

0.2

-

-

7.9

-

0.3

83

.6

-

-

16

.4

-

-

-

16.4

3,843

No

rth H

orr

Cons

tit-ue

ncy

0.0

0.4

1.8

0.3

2.0

64

.2

3.9

1.7

0.0

74.3

1.4

11

.4

12.7

0.0

0.1

0.1

25

.7

56

,230

Duka

na

-

0.1

-

-

1.1

98

.0

-

-

-

99.2

-

0.7

0.1

-

-

-

0.8

14,45

2

Maiko

na

0.0

1.5

0.0

0.0

4.0

53.2

15

.2

1.8

0.1

75.9

3.8

19

.3

0.7

0.0

-

0.3

24.1

14,34

5

Turb

i

0.0

-

-

-

-

0.3

-

9.7

-

10.1

-

0.1

89

.8

-

-

-

89.9

7,351

North

Hor

r

0.1

-

0.3

0.1

0.8

67.5

-

-

-

68

.8

1.7

26.8

2.6

0.0

0.1

-

31

.2

12

,809

Illere

t

0.1

0.1

13.1

1.9

3.8

77

.8

-

-

-

96.7

0.1

1.0

1.2

0.2

0.8

0.0

3.3

7,273

47


Saku

Con

stitue

ncy

0.1

0.3

0.0

0.1

1.0

28

.8

0.4

36.2

0.1

67

.0

4.7

5.5

11.6

0.4

9.3

1.5

33

.0

29

,020

Saga

nte/Ja

ldesa

0.0

-

-

-

2.0

47

.2

0.3

12.8

0.1

62

.5

2.4

6.7

20.0

0.1

8.1

0.2

37

.5

10

,777

Kara

re

-

-

-

-

1.5

49

.6

-

-

0.1

51.1

19

.6

4.5

9.5

-

15.3

0.1

48

.9

5,3

46

Marsa

bit C

entra

l

0.1

0.7

0.1

0.2

-

4.9

0.6

70.8

0.1

77

.5

0.4

5.0

5.4

0.7

7.9

3.2

22.5

12,89

7

Laisa

mis

Cons

tituen

cy

0.2

0.7

5.3

2.6

1.2

50.4

0.0

0.0

-

60

.5

0.6

6.3

18.5

0.4

13

.0

0.7

39.5

36,46

8

Loiya

ngala

ni

0.5

2.3

19.5

3.3

3.2

23

.1

-

-

-

51.9

-

6.7

1.2

1.4

38

.8

0.0

48.1

9,894

Karg

i/Sou

th Ho

rr

-

-

-

9.6

0.2

70

.4

-

-

-

80.2

-

1.4

7.2

0.0

11

.2

0.0

19.8

5,112

Korr/

Ngur

unit

0.0

0.4

-

1.2

0.5

72

.7

-

0.1

-

74.9

1.8

7.1

14

.2

-

0.1

1.9

25.1

12,32

5

Logo

logo

-

-

-

-

-

0.1

-

-

-

0.1

-

-

99

.9

-

-

-

99.9

2,808

Laisa

mis

-

-

-

-

0.9

55.6

0.2

-

-

56

.7

0.2

10.7

26

.8

0.4

5.3

-

43.3

6,329

Tabl

e 25.2

5: S

ourc

e of W

ater

of F

emale

hea

ded

Hous

ehol

d by

Cou

nty,

Cons

titue

ncy a

nd W

ard

Coun

ty/C

onst

ituen

cy/

War

dsPo

ndDa

mLa

keSt

ream

/Ri

ver

Unpr

o-te

cted

Sp

ring

Unpr

o-te

cted

W

ellJa

bia

Wat

er

vend

orOt

her

Unim

-pr

oved

So

urce

s

Pro-

tect

ed

Sprin

gPr

otec

ted

Well

Bore

-ho

le

Pipe

d in

to

Dwell

ing

Pipe

d

Rain

W

ater

Co

llec-

tion

Impr

oved

So

urce

sNu

mbe

r of

Indi

vidua

ls

Keny

a

2.8

2.7

1.3

25.2

5.3

7.4

0.3

4.4

0.3

49.7

8.1

7.7

11

.3

5.1

17.5

0.7

50

.3

11,1

64,58

1

Rura

l

3.4

3.5

1.6

30.6

6.5

8.9

0.3

1.8

0.4

57.0

9.5

8.0

11

.5

1.6

11.7

0.8

43

.0

8,05

8,724

Urba

n

1.0

0.8

0.6

11.1

2.3

3.4

0.2

11

.1

0.1

30

.5

4.7

7.0

10.5

14

.2

32.5

0.6

69

.5

3,10

5,857

Marsa

bit C

ounty

7.0

3.1

0.9

0.6

1.0

34

.8

1.1

10.1

0.1

58.9

1.9

11

.5

19.0

0.3

8.0

0.5

41

.1

95,09

1

Moya

le C

onsti

tuenc

y

21.1

7.9

0.3

0.1

1.2

12

.1

0.3

9.8

0.2

53

.0

1.3

15.7

24

.8

0.1

4.5

0.5

47.0

31

,301

Butiy

e

1.4

7.2

-

0.2

0.1

10.5

-

0.5

0.3

20.2

4.7

52

.5

20.6

-

1.8

0.2

79

.8

5,12

6

Solol

o

57.1

-

-

-

0.4

2.1

-

-

-

59.6

-

0.1

40

.1

-

-

0.2

40

.4

3,15

4

48



Heillu

/Man

yatta

6.7

17

.8

1.2

0.3

4.1

39.5

-

0.7

-

70.4

1.9

20

.5

6.2

-

-

1.0

29

.6

7,04

6

Golbo

38

.8

7.9

-

0.1

0.1

2.7

-

7.5

-

57

.1

-

-

26

.5

-

16.4

-

42

.9

6,94

6

Moya

le To

wnsh

ip

-

6.1

-

0.1

-

4.1

0.9

51.7

1.0

63.9

0.8

15

.0

14.6

0.5

3.9

1.3

36

.1

4,78

5

Uran

11

.4

-

-

-

2.3

-

-

-

-

13.6

-

2.2

84

.2

-

-

-

86

.4

2,89

8

Obbu

92

.3

-

-

-

-

-

4.8

-

-

97.2

-

0.2

2.6

-

-

-

2.8

1,34

6 No

rth H

orr

Cons

titu-

ency

0.0

0.6

1.1

0.2

1.1

56

.9

4.7

3.3

-

68

.0

1.6

16.8

13

.6

0.0

0.0

-

32.0

18

,439

Duka

na

-

-

-

0.1

0.3

98.3

-

-

-

98.7

0.2

1.0

0.1

-

-

-

1.3

4,14

6

Maiko

na

-

2.4

-

-

0.6

46.7

18

.3

1.9

-

69

.9

2.0

27.8

0.2

0.1

-

-

30.1

4,

787

Turb

i

0.2

-

-

-

-

-

-

17

.6

-

17

.8

0.1

0.2

81.9

-

0.1

-

82

.2

2,94

4

North

Hor

r

0.0

-

-

-

0.8

53.7

-

-

-

54.5

4.3

39

.5

1.7

-

-

-

45

.5

4,34

8

Illere

t

-

-

9.2

1.3

5.3

83.7

-

-

-

99.5

-

0.2

0.2

-

0.1

-

0.5

2,

214

Saku

Con

stitue

ncy

-

0.4

0.1

0.0

0.8

28

.8

0.4

35.3

0.2

66.0

5.0

6.0

12

.2

0.6

9.1

1.1

34.0

16

,873

Saga

nte/Ja

ldesa

-

-

-

0.1

1.3

46

.5

-

10.5

0.3

58.5

2.2

8.8

22

.9

0.2

6.8

0.6

41.5

5,

963

Kara

re

-

-

-

-

1.5

48.6

-

-

0.1

50.2

19

.6

4.3

9.9

-

16.0

-

49

.8

3,60

8

Marsa

bit C

entra

l

-

0.9

0.2

-

0.1

4.7

1.0

72.9

0.1

79.9

0.1

4.6

4.6

1.2

7.5

2.2

20

.1

7,30

2

Laisa

mis

Cons

tituen

cy

0.2

0.9

2.1

1.9

1.0

49.0

0.1

-

0.0

55.3

1.0

6.6

20

.2

0.4

16.2

0.4

44

.7

28,47

8

Loiya

ngala

ni

0.9

3.9

9.5

3.1

3.6

17.0

-

-

-

37.9

0.1

6.2

0.3

1.0

54

.4

-

62.1

6,

297

Karg

i/Sou

th Ho

rr

-

-

-

5.7

0.2

69.2

-

-

-

75.1

0.3

2.4

4.2

-

18

.0

-

24.9

4,

641

Korr/

Ngur

unit

0.0

0.3

-

0.8

0.1

72

.0

-

-

0.1

73

.2

2.4

5.4

17.7

-

0.0

1.3

26

.8

8,93

1

Logo

logo

-

-

-

-

-

0.1

-

-

-

0.1

-

-

99.9

-

-

-

99.9

2,

311

Laisa

mis

-

-

-

-

0.5

51

.7

0.5

-

-

52

.7

0.5

14.2

26

.1

0.8

5.6

-

47.3

6,

298

49


Table 25.26: Human Waste Disposal by County, Constituency and Ward

County/ Constit-uency

Main Sewer

Septic Tank

Cess Pool

VIP Latrine

Pit Latrine

Improved Sanitation

Pit Latrine Uncov-ered Bucket Bush Other

Unim-proved Sanita-tion

Number of HH Memmbers

Kenya 5.91 2.76 0.27 4.57 47.62 61.14 20.87 0.27 17.58 0.14 38.86 37,919,647

Rural 0.14 0.37 0.08 3.97 48.91 53.47 22.32 0.07 24.01 0.13 46.53 26,075,195

Urban 18.61 8.01 0.70 5.90 44.80 78.02 17.67 0.71 3.42 0.18 21.98 11,844,452

Marsabit County 0.05 0.16 0.04 2.68 23.94 26.86 11.14 0.04 61.74 0.21 73.14 288,413

Moyale Constit-uency 0.09 0.14 0.02 2.18 36.11 38.54 17.54 0.06 43.60 0.26 61.46

102,905

Butiye 0.07 0.00 0.00 0.12 50.26 50.46 0.85 0.15 48.52 0.03 49.54 17,447

Sololo 0.30 0.21 0.00 1.23 55.29 57.03 15.59 0.06 26.21 1.12 42.97 8,219

Heillu/Manyatta 0.10 0.14 0.00 4.03 9.18 13.45 63.47 0.00 23.04 0.03 86.55 21,116

Golbo 0.09 0.06 0.06 0.00 30.69 30.90 2.68 0.03 66.29 0.10 69.10 28,905

Moyale Township 0.04 0.68 0.00 9.16 78.14 88.02 7.58 0.00 4.09 0.31 11.98 12,308

Uran 0.00 0.00 0.04 0.14 22.26 22.45 11.90 0.17 65.48 0.00 77.55 9,721

Obbu 0.00 0.00 0.00 2.47 24.09 26.56 6.90 0.08 64.64 1.83 73.44 5,189

North Horr Constit-uency 0.01 0.08 0.05 3.50 9.68 13.33 5.52 0.05 80.78 0.32 86.67

74,669

Dukana 0.00 0.02 0.03 2.26 2.76 5.07 9.27 0.02 85.42 0.23 94.93 18,598

Maikona 0.06 0.06 0.15 5.59 12.75 18.61 5.15 0.00 75.88 0.36 81.39 19,132

Turbi 0.00 0.00 0.00 1.27 13.89 15.16 0.40 0.02 83.78 0.64 84.84 10,295

North Horr 0.00 0.04 0.05 5.78 15.67 21.54 7.63 0.14 70.47 0.22 78.46 17,157

Illeret 0.00 0.38 0.00 0.03 1.62 2.03 0.62 0.06 96.99 0.30 97.97 9,487

Saku Constituency 0.07 0.39 0.06 4.72 42.70 47.95 16.31 0.02 35.59 0.14 52.05 45,893

Sagante/Jaldesa 0.00 0.11 0.05 4.83 26.46 31.45 15.82 0.04 52.60 0.08 68.55 16,740

Karare 0.12 0.11 0.00 2.51 21.66 24.40 6.32 0.03 69.21 0.03 75.60 8,954

Marsabit Central 0.11 0.75 0.10 5.61 65.48 72.05 21.14 0.00 6.58 0.22 27.95 20,199

Laisamis Constit-uency 0.00 0.11 0.02 1.09 7.80 9.03 3.82 0.02 87.07 0.07 90.97

64,946

Loiyangalani 0.00 0.28 0.05 1.09 5.61 7.03 8.15 0.02 84.71 0.09 92.97 16,191

Kargi/South Horr 0.00 0.00 0.00 1.27 7.62 8.89 5.31 0.03 85.75 0.02 91.11 9,753

Korr/Ngurunit 0.00 0.03 0.00 0.71 4.45 5.18 1.79 0.00 92.95 0.07 94.82 21,256

Logologo 0.00 0.00 0.12 0.33 18.01 18.46 1.47 0.00 79.96 0.12 81.54 5,119

Laisamis 0.00 0.15 0.00 1.93 12.25 14.33 1.49 0.05 84.08 0.05 85.67 12,627

50



Table 25.27: Human Waste Disposal in Male Headed household by County, Constituency and Ward

County/ Constitu-ency/wards

Main Sewer

Septic Tank

Cess Pool

VIP Latrine

Pit Latrine

Improved Sanitation

Pit Latrine Uncovered Bucket Bush Other

Unim-proved Sanitation


Kenya 6.30 2.98 0.29 4.60 47.65 61.81 20.65 0.28 17.12 0.14 38.19 26,755,066

Rural 0.15 0.40 0.08 3.97 49.08 53.68 22.22 0.07 23.91 0.12 46.32 18,016,471

Urban 18.98 8.29 0.73 5.89 44.69 78.58 17.41 0.70 3.13 0.18 21.42 8,738,595

Marsabit County 0.06 0.19 0.04 2.69 22.77 25.76 10.69 0.04 63.31 0.20 74.24 193,322

Moyale Constitu-ency 0.10 0.18 0.03 2.26 33.32 35.89 16.31 0.07 47.54 0.19 64.11

71,604

Butiye 0.11 0.00 0.00 0.17 45.09 45.36 0.85 0.13 53.62 0.04 54.64 12,321

Sololo 0.30 0.28 0.00 1.48 56.35 58.40 14.97 0.10 25.59 0.95 41.60 5,065

Heillu/Manyatta 0.15 0.11 0.00 5.05 9.24 14.55 61.16 0.00 24.24 0.05 85.45 14,070

Golbo 0.10 0.08 0.08 0.00 27.07 27.32 2.57 0.05 69.93 0.13 72.68 21,959

Moyale Township 0.07 1.12 0.00 9.26 77.30 87.74 8.07 0.00 3.96 0.23 12.26 7,523

Uran 0.00 0.00 0.06 0.13 23.29 23.48 11.51 0.25 64.77 0.00 76.52 6,823

Obbu 0.00 0.00 0.00 2.68 20.77 23.45 6.58 0.10 69.11 0.75 76.55 3,843

North Horr Constit-uency 0.01 0.08 0.06 3.23 8.51 11.89 5.39 0.04 82.39 0.30 88.11

56,230

Dukana 0.00 0.02 0.03 1.90 2.54 4.49 9.11 0.02 86.17 0.20 95.51 14,452

Maikona 0.03 0.08 0.15 5.03 11.98 17.27 5.04 0.00 77.38 0.31 82.73 14,345

Turbi 0.00 0.00 0.00 1.47 9.84 11.30 0.37 0.00 87.73 0.60 88.70 7,351

North Horr 0.00 0.05 0.05 5.54 14.39 20.04 7.20 0.12 72.45 0.20 79.96 12,809

Illeret 0.00 0.30 0.00 0.04 1.86 2.20 0.54 0.08 96.84 0.34 97.80 7,273

Saku Constituency 0.11 0.48 0.06 4.88 43.74 49.27 16.24 0.02 34.31 0.16 50.73 29,020

Sagante/Jaldesa 0.00 0.13 0.05 5.25 27.92 33.35 15.95 0.06 50.53 0.10 66.65 10,777

Karare 0.21 0.09 0.00 2.56 20.95 23.81 6.40 0.00 69.73 0.06 76.19 5,346

Marsabit Central 0.17 0.94 0.09 5.52 66.40 73.13 20.56 0.00 6.07 0.24 26.87 12,897

Laisamis Constit-uency 0.00 0.16 0.02 0.97 7.39 8.53 3.44 0.01 87.93 0.10 91.47

36,468

Loiyangalani 0.00 0.44 0.00 1.32 5.30 7.06 6.82 0.03 85.94 0.14 92.94 9,894

Kargi/South Horr 0.00 0.00 0.00 0.88 7.14 8.02 5.26 0.00 86.72 0.00 91.98 5,112

Korr/Ngurunit 0.00 0.00 0.00 0.60 4.66 5.26 1.72 0.00 92.94 0.08 94.74 12,325

Logologo 0.00 0.00 0.21 0.18 14.25 14.64 0.71 0.00 84.44 0.21 85.36 2,808

Laisamis 0.00 0.21 0.00 1.56 13.13 14.90 1.22 0.00 83.79 0.09 85.10 6,329

51


Table 25.28: Human Waste Disposal in Female Headed Household by County, Constituency and Ward

County/ ConstituencyMain Sewer

Septic Tank

Cess Pool

VIP Latrine

Pit Latrine

Improved Sanitation

Pit Latrine Uncovered Bucket Bush Other

Unim-proved Sanitation


Kenya 5.0 2.2 0.2 4.5 47.6 59.5 21.4 0.3 18.7 0.2 40.5 11,164,581.0

Rural 0.1 0.3 0.1 4.0 48.5 53.0 22.6 0.1 24.2 0.1 47.0 8,058,724.0

Urban 17.6 7.2 0.6 5.9 45.1 76.4 18.4 0.7 4.3 0.2 23.6 3,105,857.0

Marsabit 0.0 0.1 0.0 2.7 26.3 29.1 12.1 0.0 58.6 0.2 70.9 95,091.0

Moyale 0.0 0.1 0.0 2.0 42.5 44.6 20.4 0.0 34.6 0.4 55.4 31,301.0

Butiye 0.0 0.0 0.0 0.0 62.7 62.7 0.8 0.2 36.3 0.0 37.3 5,126.0

Sololo 0.3 0.1 0.0 0.8 53.6 54.8 16.6 0.0 27.2 1.4 45.2 3,154.0

Heillu/Manyatta 0.0 0.2 0.0 2.0 9.1 11.3 68.1 0.0 20.6 0.0 88.7 7,046.0

Golbo 0.1 0.0 0.0 0.0 42.1 42.2 3.0 0.0 54.8 0.0 57.8 6,946.0

Moyale Township 0.0 0.0 0.0 9.0 79.5 88.4 6.8 0.0 4.3 0.4 11.6 4,785.0

Uran 0.0 0.0 0.0 0.2 19.8 20.0 12.8 0.0 67.1 0.0 80.0 2,898.0

Obbu 0.0 0.0 0.0 1.9 33.6 35.4 7.8 0.0 51.9 4.9 64.6 1,346.0

North Horr 0.0 0.1 0.0 4.3 13.2 17.7 5.9 0.1 75.9 0.4 82.3 18,439.0

Dukana 0.0 0.0 0.0 3.5 3.5 7.1 9.8 0.0 82.8 0.3 92.9 4,146.0

Maikona 0.1 0.0 0.1 7.2 15.1 22.6 5.5 0.0 71.4 0.5 77.4 4,787.0

Turbi 0.0 0.0 0.0 0.8 24.0 24.8 0.5 0.1 73.9 0.7 75.2 2,944.0

North Horr 0.0 0.0 0.0 6.5 19.5 26.0 8.9 0.2 64.6 0.3 74.0 4,348.0

Illeret 0.0 0.6 0.0 0.0 0.9 1.5 0.9 0.0 97.5 0.1 98.5 2,214.0

Saku 0.0 0.2 0.1 4.5 40.9 45.7 16.4 0.0 37.8 0.1 54.3 16,873.0

Sagante/Jaldesa 0.0 0.1 0.1 4.1 23.8 28.0 15.6 0.0 56.3 0.1 72.0 5,963.0

Karare 0.0 0.1 0.0 2.4 22.7 25.3 6.2 0.1 68.4 0.0 74.7 3,608.0

Marsabit Central 0.0 0.4 0.1 5.8 63.9 70.2 22.2 0.0 7.5 0.2 29.8 7,302.0

Laisamis 0.0 0.0 0.0 1.3 8.3 9.7 4.3 0.0 86.0 0.0 90.3 28,478.0

Loiyangalani 0.0 0.0 0.1 0.7 6.1 7.0 10.2 0.0 82.8 0.0 93.0 6,297.0

Kargi/South Horr 0.0 0.0 0.0 1.7 8.1 9.8 5.4 0.1 84.7 0.0 90.2 4,641.0

Korr/Ngurunit 0.0 0.1 0.0 0.9 4.2 5.1 1.9 0.0 93.0 0.1 94.9 8,931.0

Logologo 0.0 0.0 0.0 0.5 22.6 23.1 2.4 0.0 74.5 0.0 76.9 2,311.0

Laisamis 0.0 0.1 0.0 2.3 11.4 13.8 1.8 0.1 84.4 0.0 86.2 6,298.0

52



marsabit county - inequalities

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